Landfill Loadings Calculations

For Solvent-Contaminated Wipes

January 2012

Prepared for

U.S. Environmental Protection Agency

Ariel Rios Building

1200 Pennsylvania Avenue, N. W. 

Washington, DC 20460

Prepared by

SAIC

12100 Sunset Hills Road

Reston, VA 20190

EPA Contract No.: EP-W-05-060, WA No. 3-06
Table of Contents

Executive Summary ..............................................................................................................................i

1.0 Introduction to Landfill Loading Calculations...............................................................................1

1.1 How Does the Analysis Address Parameter Variability and Uncertainty? ................................1

1.2 What Chemicals are Considered? ..............................................................................................1

1.2.1 Chemical Use Restrictions of Ozone Depleting Substances...............................................3

1.2.2 Chemicals Not Known to be Used as Solvents in Wipe Applications................................4

1.3 Input Parameter Assumptions ....................................................................................................5

2.0 Solvent Loadings Calculations: Disposables .................................................................................6

2.1 How Can Solvents Enter the Environment from Disposable Wipes? ........................................6

2.2 Overview of Calculation Methodology......................................................................................6

2.3 Data Used for Assessing Individual Variables...........................................................................9

2.3.1 Number of Towels or Wipes Used by a Single Generator..................................................9

2.3.2 Quantity of Solvent in Disposed Wipe..............................................................................10

2.3.3 Percent of Wipes Containing Constituent .........................................................................12

2.3.4 Estimated Count of Spent Solvent Disposable Wipes Generators....................................16

2.3.5 Percent of Constituent in the Solvent................................................................................20

2.3.6 Number of Generators Disposing Towels or Wipes in a Single Landfill .........................21

2.4 Results......................................................................................................................................21

2.4.1 Loading Estimates.............................................................................................................21

2.4.2 Sensitivity Analysis...........................................................................................................23

2.4.3 Effects of Input Parameter Distribution Assumptions on Results.....................................26

2.4.4 Uncertainties Associated with the Analysis......................................................................29

2.4.5 Numerical Stability for Disposable Wipe Analysis ..........................................................31

2.5 Solvent Extraction Technologies .............................................................................................31

3.0 Solvent Loadings Calculations: Laundries...................................................................................32

3.1 How Can Solvents Enter the Environment from Reusable Wipes? .........................................32

3.2 Overview of Calculation Methodology....................................................................................32

3.3 Data Used for Assessing Individual Variables.........................................................................34

3.3.1 Quantity of Shop and Printer Towels Washed by Each Laundry .....................................34

3.3.2 Fraction of Laundries Processing Printer Towels Versus Shop Towels...........................35

3.3.3 Concentration in Washwater .............................................................................................35

3.3.4 Quantity of Washwater Generated ....................................................................................38

3.3.5 Sludge Partitioning............................................................................................................39

3.3.6 Number of Laundries Disposing Sludge in a Single Landfill ...........................................40

3.3.7 Estimates of Methanol Sent to Industrial Laundries.........................................................40

3.4 Results......................................................................................................................................41

3.4.1 Loading Estimates.............................................................................................................41

3.4.2 Sensitivity Analysis...........................................................................................................43

3.4.3 Effects of Input Parameter Distribution Assumptions on Results.....................................44

3.4.4 Uncertainties Associated with the Analysis......................................................................47

3.4.5 Numerical Stability for Laundry Sludge Analysis............................................................48

4.0 References....................................................................................................................................49

Appendix A. Supporting Data...........................................................................................................53

Appendix B. Data Values Used in Disposables Analysis.................................................................64

Appendix C. Industry-Specific Use of F-Listed Solvents – NESHAPs............................................71

Appendix D. 2003 Biennial Report Data ..........................................................................................75

Appendix E. Federal and State Right-to-Know Data for Evaluating Solvent Use............................82
Appendix F. Industrial Laundry Data................................................................................................89

Appendix G. Review of Other Industry Data for Estimated Number of Generators......................107

Appendix H. Input Parameter Distributions for Disposable Wipes Analysis.................................109

Appendix I. Input Parameter Distributions for Laundry Sludge Analysis......................................127

Appendix J. Bootstrap Estimates of 95% Confidence Intervals of Estimated Landfill Loadings ..146
i

Executive Summary

Purpose

A wide variety of industries use solvent containing wipes for cleaning and degreasing. The wipes 

may be used once or several times before they are thrown away, while other wipes are used, 

laundered, and reused. When discarded, spent wipes are considered hazardous waste under the 

federal hazardous waste regulations if the wipes exhibit a hazardous waste characteristic under 40 

CFR 261, Subpart C or contain a solvent listed in 40 CFR 261.31.

To support a rulemaking to exclude the wipes from the definitions of hazardous waste and solid 

waste, the Environmental Protection Agency (EPA) conducted a risk analysis. The analysis 

determines the potential risks from two solvent-contaminated wipes management scenarios: the 

disposal of solvent-contaminated wipes at municipal solid waste landfills (MSWLFs), and the 

disposal of solvent-contaminated wastewater treatment sludge from industrial laundries at 

MSWLFs. 

This report presents estimates of the quantities of solvent present in solvent-contaminated wipes and 

industrial laundry sludge that could be disposed in MSWLFs. A draft of this report was externally 

peer reviewed by four independent experts in accordance with EPA’s peer review policy. This 

report incorporates revisions made in response to the peer reviewers. The peer review record (i.e., 

the charge, profile of peer reviewers, the peer review comments, and a written response to the peer 

review addressing each comment) is available in the docket for this rule.

Chemicals Evaluated

For this analysis, 30 chemicals were initially considered; the 30 chemicals correspond to those listed 

in 40 CFR 261.31 (EPA hazardous waste numbers F001, F002, F003, F004, and F005). These 30 

chemicals are identified in Exhibit ES-1. A screening evaluation was conducted to determine the 

likelihood that each chemical would be used in conjunction with wipes, based on solvent use 

patterns and current regulations affecting solvent use. As a result of this evaluation, 10 of the 30 

chemicals were eliminated from this evaluation (i.e., loadings were estimated as zero); further 

quantitative assessment was conducted for the 20 remaining chemicals.

Approach

This report estimates landfill loadings (i.e., the quantities of solvents disposed in a MSWLF) under 

two management scenarios: the disposal of solvent-contaminated wipes in a MSWLF, and the 

disposal of solvent-contaminated laundry sludge in a MSWLF. The report identifies the evaluation 

approach, data collection activities, and assumptions used to estimate landfill loadings for the 

solvent chemicals in each of the two scenarios.

For each of the two management scenarios, a model equation was developed. Input parameters 

(discussed below) were assigned empirical data-based probability distributions to account for the 

uncertainty and variability in the parameter values. The available data exhibits inherent uncertainty 

resulting from empirical data that is not specifically representative of all operations within all 

generator facilities. In addition, there is a wide range of variability in possible solvent combinations 

and how solvents are used. 
ii

The landfill loading results for both scenarios are presented as distributions from a probabilistic 

(Monte Carlo) analysis method. EPA’s Risk Assessment Guidance for Superfund Volume III Part 

A, Process for Conducting Probabilistic Risk Assessment was used to guide the assessment process 

with regard to selecting probability distributions, evaluating sensitivity, and assessing the numerical 

stability of the results.

Evaluation of Disposal of Solvent-Contaminated Wipes in a Landfill

Many different types of facilities (or sectors) generate solvent-contaminated wipes. The use 

patterns (including the number of facilities, number of wipes used by each facility, quantities of 

solvent used, and types of solvents used) vary between sectors as well as among facilities in a single 

sector. In addition, disposal patterns such as the number of facilities sending waste to a single 

landfill will also affect the quantities of chemicals present in solvent-contaminated wipes disposed 

in MSWLFs.

The evaluation quantitatively considered differences among principal solvent use sectors (e.g., the 

printing sector vs. the automobile repair sector) to the extent data were available. The principal 

input parameters considered were as follows: 

▪ Number of disposable wipes generated by a single facility

▪ Quantity of solvent on a disposed wipe

▪ Percent of wipes in a sector containing each evaluated chemical 

▪ Number of generators

▪ Fraction of each constituent in solvent, and 

▪ Number of generators using a single landfill for disposal. 

Evaluation of Disposal of Laundry Sludge in a Landfill

Some solvent-contaminated wipes are sent to industrial laundries, where the wipes are cleaned and 

reused. When laundered, the wipes contribute pollutants (i.e., solvent contaminants) to a laundry’s 

pretreatment system. Laundries may be required to control these pollutants prior to discharge to a 

publicly owned treatment works (POTW). Once removed from the pretreatment system, any 

generated sludges, semi-solids and solids are managed as solid wastes using management methods 

such as disposal in MSWLFs.

Input data used in the laundry sludge evaluation are from Textile Rental Services Association of 

America (TRSA) 2005 to 2009 survey data and from effluent guidelines development for industrial 

laundries, finalized in 2000. Data were available for two types of industrial wipes: shop towels and 

printer towels. These differences were incorporated into the evaluation of this scenario. As noted 

above, uncertainty and variability of the input parameters were addressed in most cases by assigning 

probability distributions. The principal input parameters considered and the data sources used are 

discussed below:

▪ Quantity and distribution of wipes washed at each laundry

▪ Chemical concentrations of solvent in washwater

▪ Sludge partitioning factor (i.e., the fraction of solvent in the washwater that will be 

present in the disposed sludge), and

▪ Number of laundries using a single landfill for disposal. 
iii

Results

For each of the 20 chemicals in each of the two scenarios, calculations were performed using both 

deterministic (point-value) and Monte Carlo methods. The deterministic calculations were 

conducted by setting all input parameters to their average values. The Monte Carlo results for each 

of the two scenarios are shown in Exhibit ES-1. The values in Exhibit ES-1 represent the quantity 

of each chemical estimated to be disposed in a single landfill, for each scenario. Based on the 

results of conducting multiple Monte Carlo analyses on the same set of input parameters, the 

probabilistic results for each of the two management scenarios were found to be numerically stable.

A separate sensitivity analysis was conducted to identify the input parameter assumptions that have 

the greatest influence affecting the final results. Based on these results, additional analyses were 

conducted to evaluate data uncertainty for the most sensitive input parameters. Uncertainty was 

evaluated with respect to the assumption of the type of probability distribution used for the input 

data, and to evaluate possible outliers within the input data set. Results from the sensitivity and 

uncertainty analyses include the following:

• For the disposal of solvent-contaminated wipes in a landfill, sensitivity was evaluated for 16 

input parameters for each of the 20 chemicals. The most sensitive parameter was typically the 

concentration of chemical in the solvent. The most significant effects on results for the 

uncertainty analysis occurred when changes in the assumptions for this input parameter were 

made. Six input parameters (which included solvent concentration as well as five other input 

parameters) were found to be consistently more sensitive than the others evaluated.

• For the disposal of laundry sludge in a landfill, two input parameters were identified to be the 

most sensitive: the number of shop towels washed by each laundry, and the chemical 

concentration in the washwater generated from the shop towels. In the uncertainty analysis, the 

effect of changing various input data assumptions was that the subsequent changes in the results 

were less pronounced than those found for the solvent-contaminated wipes disposal scenario.

Uncertainty in the results is affected by uncertainties or limitations in the input data, and certain 

assumptions made to simplify the analysis. These uncertainties include the following:

• High variability is expected for several parameters due to the wide variety of facilities and 

industries generating solvent-contaminated wipes. Data are not always available to fully 

characterize these differences.

• Some of the data are somewhat dated and may or may not reflect present-day practices. Most 

input parameters are based on ‘present day’ characteristics or data which was current at the time 

it was collected. Even in cases where trends can be assumed, it is difficult to quantify the 

magnitude of these trends.

• In general, conservative assumptions were used in estimating the quantity of solvent which may 

escape from the wipe (due to evaporation and on-site removal of excess solvent) in the scenario 

assessing disposal of solvent-contaminated wipes in a landfill.
iv

• Insufficient data were available to evaluate variability within certain input parameters. For 

example, the concentration of a chemical (e.g., toluene) in a solvent used by the printing 

industry may be different from the concentration in solvents used by the automotive industry.

Exhibit ES-1. Quantities of Chemicals Present in Solvent-Contaminated Wipes and Industrial 

Laundry Sludge Disposed in MSW Landfills

Constituent Landfill Loading Results for 

Disposal of Wipes

Result per Landfill (kg/yr)

Landfill Loading Results for 

Disposal of Laundry Sludge

Results per Landfill (kg/yr)

50th

Percentile

90th

Percentile

95th

Percentile

50th

Percentile

90th

Percentile

95th

Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane 

Ethyl ether

Nitrobenzene

2-Nitropropane

Pyridine

1,1,1-Trichloroethane

1,1,2-Trichlorotrifluoroethane

Trichlorofluoromethane

0 0 0 0 0 0

Acetone 1,033 9,120 16,224 2.53 26.40 53.02

Benzene 1.8 12.1 20.2 0.21 2.36 4.53

n-Butanol 61 607 1,146 0.13 1.36 2.61

Chlorobenzene 56 431 752 0.08 1.06 2.17

Cresols 9 58 103 0.11 1.14 2.16

Cyclohexanone 159 1,207 2,102 0.35 3.70 7.07

1,2-Dichlorobenzene 46 364 631 0.11 1.22 2.34

Ethyl acetate 52 414 733 0.17 1.96 3.89

Ethyl benzene 184 1,857 3,446 7.81 96.37 193.92

2-Ethoxyethanol 222 2,606 4,974 0.21 2.34 4.59

Isobutyl alcohol 31 220 376 0.11 1.21 2.35

Methanol 225 2,431 4,747 3.23 5.77 6.57

Methyl ethyl ketone 1,307 11,676 20,532 1.72 32.96 77.87

Methyl isobutyl ketone 150 2,073 4,184 1.00 10.52 19.54

Methylene chloride 465 3,268 5,583 9.25 51.02 78.46

Tetrachloroethylene 107 727 1,226 19.71 93.00 139.35

Toluene 1,215 8,918 15,180 6.10 43.05 69.22

1,1,2-Trichloroethane 40 330 581 0.21 2.33 4.53

Trichloroethylene 308 1,911 3,146 0.17 2.54 5.26

Xylenes (total) 958 8,556 15,184 4.65 21.98 33.26

For 10 chemicals in Table ES-1, results are zero because no use was assumed.
1

1.0 Introduction to Landfill Loading Calculations

This report describes the approach used in evaluating the landfill loading from the management of 

solvent-contaminated wipes in a landfill. The purpose of these calculations is to develop 

constituent-specific estimates of the quantity of chemical disposed in a landfill under two scenarios: 

(1) disposal of wipes in a municipal solid waste (MSW) landfill; and (2) disposal of industrial 

laundry sludge in a MSW landfill, generated from the washing of wipes. These results are used as 

inputs to a risk analysis to assess the potential risks posed by these chemicals in the landfill 

environment.

1.1 How Does the Analysis Address Parameter Variability and Uncertainty?

There are a large number of possible combinations of solvent blends used by generating facilities 

for cleaning and degreasing operations. Some generators use blends consisting of three or four 

constituents, others use pure solvents. Also, many generators use very few wipes per day while 

many others use thousands. In effect, no two generators are the same in terms of their solvent use 

practices. In addition, the available data will exhibit uncertainty as a result of empirical data that 

are not completely representative either of present day operations or of all facilities.

To account for these effects, variability is assessed using probability distributions in a Monte Carlo 

analysis. The analysis was conducted using CrystalBall®, user-friendly software that facilitates the 

performance of Monte Carlo-type analyses by linking data to Excel spreadsheets. Input values were 

assigned a distribution based on available empirical data. In cases where there was no basis for 

estimating variability or uncertainty of a specific parameter, the value was constant (this is not 

intended to mean that no uncertainty exists, but instead means that uncertainty was not assessed). 

Each iteration used different input values based on these input distributions. The resulting 

calculations produce a range (distribution) of results to reflect the uncertainty of the input values. 

All Monte Carlo analyses were conducted with 100,000 iterations.

In addition, a separate sensitivity analysis is conducted. The sensitivity analysis assesses the 

influence that each input parameter has on the final result. These results identify which 

assumptions are the most important or least important. Because many assumptions are used in the 

calculations, the sensitivity analysis is useful for identifying which of these assumptions have the 

greatest effect on influencing the results.

1.2 What Chemicals are Considered?

For this analysis, 30 chemicals were initially considered, corresponding to the constituents 

identified in the F001 to F005 listing. These chemicals are identified in Exhibit 1-1. These 

constituents were selected because they are expected to be present in mixtures of hazardous waste 

spent solvent (e.g., F001 to F005) and wipes.

Exhibit 1-1 identifies that quantitative loading estimates were subsequently made for 20 of these 30 

chemicals. The 30 chemicals were carefully evaluated to identify instances where the chemical 

would potentially be used in conjunction with a towel or wipe. This led to the elimination of certain 

chemicals from further consideration. These chemicals include:
2

• Ozone depleters (five chemicals).

• Chemicals not found to be used as solvents in conjunction with towels or wipes (five 

chemicals).

Sections 1.2.1 and 1.2.2 below present the data and information used for eliminating these 

chemicals from the assessment. These chemicals may still be used as solvents, or may have been 

used as solvents in conjunction with towels or wipes in the past. However, no information is 

available to suggest that these chemicals are presently used in applications where contact with a 

towel or wipe is likely. There is uncertainty with this step because there is never a way to ‘prove’ 

that a constituent is absent from formulations.

Exhibit 1-1 does not identify the following types of additional constituents potentially present on 

soiled wipes; these following types of chemicals were not assessed:

• Other hazardous solvents (such as P- and U-listed commercial chemical products) or non-Flisted ignitable-only solvents.

• Other contaminants present on a soiled wipe (e.g., metals).

Exhibit 1-1. F-List Constituents

CAS No. Constituent RCRA Waste 

Code Loading Estimate Made?

56-23-5 Carbon tetrachloride F001 No (ODS)

75-71-8 Dichlorodifluoromethane a F001 No (ODS)

108-90-7 Chlorobenzene F002 Yes

95-50-1 1,2-Dichlorobenzene F002 Yes

75-09-2 Methylene chloride F001, F002 Yes

127-18-4 Tetrachloroethylene F001, F002 Yes

79-01-6 Trichloroethylene F001, F002 Yes

71-55-6 1,1,1-Trichloroethane F001, F002 No (ODS)

79-00-5 1,1,2-Trichloroethane F002 Yes

76-13-1 1,1,2-Trichlorotrifluoroethane a F002 No (ODS)

75-69-4 Trichlorofluoromethane a F002 No (ODS)

67-64-1 Acetone F003 Yes

71-36-3 Butanol F003 Yes

108-94-1 Cyclohexanone F003 Yes

141-78-6 Ethyl acetate F003 Yes

100-41-4 Ethyl benzene F003 Yes

60-29-7 Ethyl ether F003 No (use with wipes is unlikely)

67-56-1 Methanol F003 Yes

108-10-1 Methyl isobutyl ketone F003 Yes

1330-20-7 Xylenes (total) F003 Yes

1319-77-3 Cresols F004 Yes

98-95-3 Nitrobenzene F004 No (use with wipes is unlikely)

71-43-2 Benzene F005 Yes

75-15-0 Carbon disulfide F005 No (use with wipes is unlikely)

110-80-5 2-Ethoxyethanol F005 Yes

78-83-1 Isobutyl alcohol F005 Yes
3

CAS No. Constituent RCRA Waste 

Code Loading Estimate Made?

78-93-3 Methyl ethyl ketone F005 Yes

79-46-9 2-Nitropropane F005 No (use with wipes is unlikely)

110-86-1 Pyridine F005 No (use with wipes is unlikely)

108-88-3 Toluene F005 Yes

a. The F001 listing identifies ‘chlorinated fluorocarbons.’ Dichlorodifluoromethane was selected as one such 

compound. Other chlorofluorocarbons are explicitly identified in the F002 listing.

ODS: ozone-depleting substance.

1.2.1 Chemical Use Restrictions of Ozone Depleting Substances

Some F001 to F005 listed chemicals have been heavily restricted in their production or use because 

they are ozone-depleting substances (ODS) or present other serious hazards. These include the 

following chemicals:

• Carbon tetrachloride

• 1,1,1-Trichloroethane (methyl chloroform)

• Trichlorofluoromethane (CFC-11)

• Dichlorodifluoromethane (CFC-12)

• 1,1,2-Trichlorotrifluoroethane (CFC-113)

As a result of these restrictions, it is expected that present-day use of these chemicals as components 

of solvents used in conjunction with towels or wipes is negligible. Note that carbon tetrachloride 

has restrictions with regard to both the Consumer Product Safety Commission and the Clean Air 

Act (described below).

Consumer Product Safety Commission Restrictions

The Consumer Product Safety Commission regulations, 16 CFR 1500.17(a)(2) states the following 

concerning the use of carbon tetrachloride:

‘Under the authority of section 2(q)(1)(B) of the act, the Commission declares as 

banned hazardous substances the following articles because they possess such a 

degree or nature of hazard that adequate cautionary labeling cannot be written and 

the public health and safety can be served only by keeping such articles out of 

interstate commerce: 

...Carbon tetrachloride and mixtures containing it (including carbon tetrachloride 

and mixtures containing it used in fire extinguishers), excluding unavoidable 

manufacturing residues of carbon tetrachloride in other chemicals that under 

reasonably foreseeable conditions of use do not result in an atmospheric 

concentration of carbon tetrachloride greater than 10 parts per million.’
4

Ozone Depleting Substances

The Montreal Protocol identifies ozone-depleting substances and provides a set of agreements for 

participating countries to follow in eliminating their use of these chemicals (Montreal, 2000). Many 

of these conditions were written into Title VI of the U.S. Clean Air Act (EPA, 1990). The ultimate 

goal of these restrictions is the complete elimination of these chemicals with replacement products 

and a reduction in demand.

The Clean Air Act requires EPA to phase out the production and consumption of ODS, subject to 

certain exemptions. The phase-out schedule is in Exhibit 1-2.

Exhibit 1-2. Clean Air Act Use Restrictions

Chemical CAA Due Date

Carbon tetrachloride, CFC-11, CFC-12, CFC-113 By January 1, 2000, production must cease

1,1,1-Trichloroethane By January 1, 2002, production must cease

1.2.2 Chemicals Not Known to be Used as Solvents in Wipe Applications

Several chemicals identified in Exhibit 1-1 are not known to be used as solvent mixtures in 

conjunction with towels or wipes. While it is difficult to ‘prove’ that a constituent is absent from 

solvent formulations, material contained in the RCRA-2003-0004 docket, EPA’s Biennial 

Reporting system, Toxic Reduction Inventory (TRI) reporting system, internet search, as well as 

materials from related EPA rulemaking activities (primarily NESHAPs) were reviewed to identify if 

some F-listed chemicals can be eliminated from further consideration.

All chemicals in Exhibit 1-1 were used as solvents at one time. However, some chemicals may 

have been used strictly as industrial (process) solvents where contact with towels and wipes was not 

typical. In addition, solvent formulations may have changed over the years to reduce their toxicity, 

hazardous air pollutant (HAP) content, or volatile organic compounds (VOC)-content. 

One purpose of the data collection activities identified throughout this report (including review of 

resources identified in the Appendices) was to verify if in fact the constituents listed in Exhibit 1-1 

are present in formulations likely to be used in conjunction with wipes. For five chemicals listed 

below, no use in conjunction with wipes could be found. Information for these chemicals is limited 

to identification of solvent use information, which may be old or outdated. The subsequent review 

of more recent information (e.g., from NESHAP support data discussed in Section 2.3.3) did not 

confirm that these chemicals were still used as solvents in conjunction with wipes. As a result, the 

following five chemicals are assumed to be absent from present-day applications of solvents used in 

conjunction with wipes and were not assessed in this report:

• Carbon disulfide. Carbon disulfide has been used as a solvent in extraction processes such as 

preparing fats, lacquers, and camphor. Even as early as the 1970’s, however, carbon disulfide 

was identified as having been replaced by other solvents for many applications (ATSDR, 1996). 

Carbon disulfide is a HAP.1

 1

 Solvents classified as HAPs are more likely to be discussed in NESHAP technical documents such as those identified 

in Section 2.3.3 and Appendix C. These chemicals were not mentioned in these documents.
5

• Ethyl ether. Ethyl ether is identified as an industrial solvent in applications such as the 

production of waxes, nitrocellulose, fats, and other products (Spectrum, 2001). Ethyl ether is 

not a HAP.

• Nitrobenzene. The primary use of nitrobenzene is in the captive production of aniline. 

Nitrobenzene is also used as a solvent in petroleum refining, in the manufacture of cellulose 

ethers and acetate, in the synthesis of other organic compounds including acetaminophen 

(Tylenol®), and as a solvent for shoe dyes (ATSDR, 1990). Nitrobenzene is a HAP.

• 2-Nitropropane. 2-Nitropropane is identified as a solvent in coating (e.g., printing inks and 

adhesives), paint and varnish removal, and a chemical intermediate (Spectrum, 2001). 2-

Nitropropane is a HAP.

• Pyridine. Pyridine is a solvent in paint and rubber preparation and as a solvent in research 

laboratories. Pyridine is a chemical intermediate for various end products (ASTDR, 1992). 

Pyridine is not a HAP.

Information for other chemicals was used to determine that their use as solvents in conjunction with 

wipes is more likely. This information is presented in Sections 2.3 and 3.3. Two additional 

chemicals, cyclohexanone and 1,1,2-tichloroethane, are also assumed to be used as solvents in 

conjunction with wipes although very little quantitative data were available. They have been 

identified as used in printing.

1.3 Input Parameter Assumptions

For both the disposable wipes and laundry sludge analyses, assumptions are made regarding the 

distribution of the values of the input parameters; these assumptions are incorporated into the inputs 

to the probabilistic (Monte Carlo) analysis described in Section 1.1. These assumptions allow the 

evaluation of input parameter variability in the analysis. The underlying data for these parameters 

are detailed in the subsequent sections of this report. 

For each parameter, one of the following methods was used for assigning input parameter 

distributional assumptions:

• Fixed values. These input parameters were constant in all iterations. This is not necessarily 

intended to mean that no uncertainty exists, but instead means that uncertainty was not assessed 

because there was no basis for estimating uncertainty. For example, in the disposable wipes 

analysis (Exhibit 2-1), 24 percent of all generators are assumed to be printers; this percentage 

was used for all chemicals.

• Continuous distribution: Some input variables use a continuous probability distribution based on 

empirical data (examples of distribution types include normal, lognormal, and gamma). For 

example, multiple empirical data points are available for identifying the number of disposable 

wipes used at printer sites. A ‘goodness of fit’ is performed to determine the best fit between 

these data points and standard probability distributions.2

 

 2

 The single best-fit distribution for each data set was selected within the CrystalBall® software based on the AndersonDarling ranking method (Decision Engineering, 1996). 
6

In most cases, the distribution with the highest-ranking fit was selected and subsequently used. 

The reason for this is that, for most input parameters, no mechanistic basis exists to assess 

which distribution assumption is most appropriate. The impact of this assumption, when two or 

more distribution assumptions would be appropriate for the data, is evaluated in Sections 2.4.3 

and 3.4.3 for certain input parameters in the disposable wipes and laundry sludge analyses, 

respectively. Appendices H and I presents the distributions used for each parameter and 

relevant statistics in the disposable wipes and laundry sludge analyses, respectively.

For the concentration terms used in the laundry sludge analyses, the distribution with the 

highest-ranking fit was not necessarily used; the choice of distribution for this parameter is 

discussed in Section 3.3.3.

• Uniform distributions. In some cases, two empirical data points are available for an input 

parameter. Variability is assessed by assuming that all values between these extremes have an 

equal (uniform) probability. For example, as indicated in Section 2.3.3, the percent of all 

printers assumed to use acetone is estimated as a uniform range of 16 to 26 percent. The 

analysis assumes that any percentage between these extremes is equally probable, and that there 

is no probability for a value outside this range. As with the fixed values discussed above, no 

data are available to form a basis for expanding the range.

• Triangular distributions. In cases where three empirical data points are available, a triangular 

distribution (based on the minimum, mean, and maximum values) is developed when no other 

information concerning the distribution is available. These cases are identified in Sections 3 and 

4.

2.0 Solvent Loadings Calculations: Disposables

2.1 How Can Solvents Enter the Environment from Disposable Wipes?

This assessment considers potential risks resulting from the disposal of solvent-contaminated wipes 

in a MSW landfill. Potential risks may result from the release of solvent components from the 

wipes while in the landfill. As with other analyses conducted in this report, only the compounds 

that conform to the definition of the F001 through F005 solvent listing are considered (i.e., other 

non-listed solvents and co-contaminants are not considered).

The purpose of this section is to describe the methodology used to generate landfill loading 

estimates resulting from the disposal of wipes in a municipal solid waste landfill, and present those 

results. These landfill loading estimates serve as the basis for subsequent fate and transport 

modeling.

2.2 Overview of Calculation Methodology

The purpose of the calculation is to estimate the quantity of chemical disposed in a single landfill 

per year. The overall calculation approach is presented below in Exhibit 2-1. As shown in Exhibit 

2-1, the analysis distinguishes between different types of generators (e.g., printers) when possible. 

This is because different industries use different quantities of wipes, different quantities of solvents, 

or different types of solvent chemicals.
7

The values listed in Exhibit 2-1 (in the column labeled ‘Avg Value’) are the values used in the 

deterministic analysis. The column labeled ‘Distributional Assumption’ in Exhibit 2-1 refers to 

how input parameter variability is addressed in the probabilistic (Monte Carlo) analysis described in 

Section 1.1. Refer to Section 1.3 for discussion of these assumptions. The calculation approach 

includes the following underlying assumptions:

• Consistent with the Economic Analysis (Document No. RCRA-2003-0004-0004), 75 percent of 

states are assumed to adopt the exclusion and all disposable wipes generated by large quantity 

generators (LQGs) and small quantity generators (SQGs) in these states are assumed to enter the 

nonhazardous waste stream.3 This is a conservative assumption because it is possible that some 

generators that presently manage these materials as a hazardous waste will continue to do so in 

the future, regardless of the regulatory status of the waste.

• No additional solvent removal technologies are considered (e.g., centrifuging) beyond presentday practices. In general, data were obtained from site visits and other data collection efforts, 

which account for ‘status quo’ solvent reduction techniques such as screen bottom drums, hand 

wringing, and low application rates. The current use of centrifuge and similar solvent removal 

techniques for towels and wipes is not known and was not included in the calculation. 

Similarly, projected future use of this technique was not included in the calculation. A 

qualitative discussion on this management method is included in Section 2.5.

The analysis only accounted for wipes generated by LQGs and SQGs. It does not account for the 

potential introduction of the constituent by conditionally exempt small quantity generators 

(CESQGs), household hazardous waste, or applications unrelated to solvent wipes.

 3

 States are not obligated to adopt an exclusion for wipes (if finalized). In their comments to the proposed rule, the 

Association of State and Territorial Solid Waste Management Officials (ASTSWMO), “five of the twelve States 

represented on the Task Force do not anticipate that such an exclusion from the definition of hazardous waste for 

solvent-contaminated disposable wipes would be adopted in their respective States” (Document No. RCRA-2003-0004-

0325). This does not precisely match the percentage used in the Economic Analysis but supports why a percentage less 

than 100 percent was chosen.
8

Exhibit 2-1. Sample Calculation for Acetone

Parameter Avg Value Distributional Assumption

Printing (EA Sector # 1, 10, 11)

A Number of disposables used per 

site per year 129,990 Gamma distribution

B Solvent Loading, g/towel 37 Weibull distribution

C % wipes containing chemical 21% Chemical-dependent, uniform distribution

D % Generators in sector 24% Constant

Transportation (EA Sector # 7)

A Number of disposables used per 

site per year 73,819 Lognormal distribution

B Solvent Loading, g/towel 39 Weibull distribution

C % wipes containing chemical 21% Chemical-dependent, uniform distribution

D % Generators in sector 1% Constant

Auto repair (EA Sector # 12)

A Number of disposables used per 

site per year 12,964 Triangular distribution

B Solvent Loading, g/towel 32 Uniform distribution

C % wipes containing chemical 21% Chemical-dependent, uniform distribution

D % Generators in sector 14% Constant

Electronics (EA Sector # 6)

A Number of disposables used per 

site per year 237,000 Uniform distribution

B Solvent Loading, g/towel 6 Uniform distribution

C Fraction wipes containing chemical 21% Chemical-dependent, uniform distribution

D % Generators in sector 0.5% Constant

Other (EA Sector # 2, 3, 4, 5, 8, 9, 13)

A Number of disposables used per 

site per year 66,436 Lognormal distribution

B Solvent Loading, g/towel 39 Weibull distribution

C % wipes containing chemical 21% Chemical-dependent, uniform distribution

D % Generators in sector 61% Constant

Common for all sectors

E % constituent in solvent 44% Chemical-dependent; distribution depends 

on the chemical (refer to Appendix H)

F Number of generators/landfill 19.0 Gamma distribution

Total chemical per landfill (kg/yr) 4,966 [Σ (A× B× C× D)] × (E×F)/1000

“EA Sector” refers to the sector numbers in the Economic Analysis (EPA, 2003), further discussed in Section 2.3.4.

Exhibit 2-1 identifies that data for different industries were used as available; these sectors 

correspond to those evaluated in the Economic Analysis (EPA, 2003). Exhibit 2-2 identifies the 

data used in selecting parameter values for the variables referenced in the above equation. These 

variables and source data are discussed in the following sections.
9

Exhibit 2-2. Source of Data for Disposables Calculations

Variable Report 

Section

Primary Sources How is variability assessed?

A - Number of 

towels or wipes 

used by single 

generator per year

2.3.1 Compiled from site visits, comments, 

and correspondence. See Table A-1.

Empirical data were sorted into each of 

five industries (based on data 

availability) and best fit to distributions.

B - Quantity of 

solvent per towel 

or wipe

2.3.2 Compiled from site visits, comments, 

and correspondence. See Table A-2.

Empirical data were sorted into each of 

five industries (based on data 

availability) and best fit to distributions.

C - % wipes 

containing 

chemical

2.3.3 2003 Biennial Report, NESHAP 

development data, and 2004 TRI data.

Input is a range reflecting two data 

sources. Different values were used for 

each of the five industries based on data 

availability.

D - % Generators 

in sector 2.3.4 Economic Analysis (EPA, 2003). Values were fixed.

E - % constituent 

in solvent 2.3.5

Data from 175 sites used in developing 

EPA's Alternative Control Techniques 

Document - Industrial Cleaning 

Solvents (EPA, 1994a).

Empirical data for the percent of 

chemical found in formulations were 

best fit to distributions.

F – Number of 

generators using a 

single landfill for 

disposal

2.3.6

Derived from Economic Analysis - for 

number of generators (EPA, 2003), 

Simmons - for number of landfills by 

state (Simmons, 2006), and 2002 

Census - for distribution of generating 

industries by state (Census, 2002).

Empirical averages were developed for 

each state and best fit to a distribution.

2.3 Data Used for Assessing Individual Variables

2.3.1 Number of Towels or Wipes Used by a Single Generator

The number of disposable wipes used by individual sites varies based on facility size and industry. 

Much anecdotal data are available in the RCRA-2003-0004 docket from site visits, public 

comments, and other materials. These data are tabulated in Exhibit A-1 of Appendix A and Exhibit 

B-1 of Appendix B. The data are summarized below by sector:

• Auto body shops: 7,200 to 24,000/ year (3 values from available sources).

• Transportation (includes aerospace and new vehicle assembly): 7,500 to 300,000/ year (13 

values from available sources).

• Printing: 1,000 to 600,000/ year (21 values from available sources).

• Electronics: 24,000 to 450,000/ year (2 values from available sources).

• All other manufacturing: 480 to 400,000/ year (23 values from available sources).

These data are intended to represent the quantity of wipes generated as a result of use with solvents 

(including F-listed and other solvents such as nonhazardous solvents). When available, data 

specific to the quantity of disposable wipes and reusable wipes were used. An assumption was 

made that similar facilities (i.e., printers) would use approximately the same number of wipes per 

facility whether the wipes were disposable or reusable. 
10

2.3.2 Quantity of Solvent in Disposed Wipe

As with the quantity of wipes used by a site, the quantity of solvent in a disposed wipe will vary. 

Exhibit A-2 of Appendix A and Exhibit B-2 of Appendix B include data regarding the amount of 

solvent on wipes based on anecdotal data. The available data are summarized below by sector:

• Printing: 9-86 grams (9 values from available sources)

• Electronics: 6-7 grams (2 values from available sources)

• Transportation: No data; assume similar to ‘all other manufacturing’4

• Auto Repair: 21-42 grams (2 values from available sources)

• All other manufacturing: 10 to 97 grams (7 values from available sources)

The available data are obtained from various activities, such as the quantity applied, the quantity 

disposed, or the quantity recovered from a solvent recovery operation (and therefore which would 

have remained with the towels if not recovered). Where information clearly indicates that the data 

refers to the quantity of solvent applied to the wipe, allowances were made to account for typical 

solvent removal processed, evaporation, and removal (e.g., ‘hand wringing’) prior to disposal. 

Exhibit B-2 in Appendix B indicates the use of these assumptions.

To account for evaporation in these instances, it was conservatively assumed that 78 percent of the 

solvent applied would remain on the wipe, using data from EPA (1997). This data source measured 

evaporation rates of five solvents, plus methyl ethyl ketone (MEK), under various simulated 

conditions and included differences in containers and wipes. The data from EPA (1997) are 

presented in Exhibit B-6 in Appendix B. The value of 78 percent is a calculated average 

evaporation rate for the five solvents, excluding MEK. The solvents in Exhibit B-6 are intended to 

represent the variation in physical properties of various solvents containing F001-F005 constituents. 

The reason that the MEK data were not used is because the results from this study indicated 100 

percent evaporation. All wipers used in conjunction with MEK were KimWipes. The KimWipes 

used with MEK retained 0% of the solvent, probably because of the volatility of the solvent and the 

relatively low adsorption of the wiper (EPA, 1997). For the analysis presented in the present report, 

assuming that all the solvent evaporates would not be conservative and not representative of other 

types of wipes. The low evaporation rates for the remaining solvents may be attributed to the 

limited exposure of the wipe surface to ambient air, and /or limited air circulation.

In actuality, evaporation is influenced by multiple factors including solvent properties (e.g., vapor 

pressure), wipe properties, storage and handling practices, use, and temperature (particularly indoor/ 

outdoor). Different solvents (and different components within these solvents) will evaporate at 

different rates.

 4

 Appendix A identifies data on application rates used in the transportation sector from Document No. RCRA-2003-

0004-0119; however, these data are not used. This source, a 1999 letter from the Aerospace Industries Association, 

provides somewhat contradictory information. They state that ‘typically small amounts of solvent are used relative to 

the rag's saturation capacity’ with a ‘few milliliters (50 - 250ml) per application.’ The quantity range of 50 to 250 ml 

(approximately 45 to 225 grams, assuming a solvent density of 0.9 g/ml) is much higher than expected from these 

qualitative statements. The range of 45 to 225 grams per wipe is an outlier value when compared to the other data listed 

here.
11

Exhibit 2-3 summarizes the volatility of the 20 evaluated solvent chemicals (a higher vapor pressure 

indicates a more volatile chemical). These data should be used as a guide for evaluating the effect 

of the evaporation assumption. This assumption is expected to be a conservative approach for 

volatile solvents such as pure MEK, or pure chemical solvents such as acetone which are more 

volatile than MEK. As noted above, many factors, including other components in the solvent, will 

influence the actual evaporation rate.

Exhibit 2-3. Vapor Pressures and Specific Gravities of Solvent Chemicals

CAS No. Constituent Vapor Pressure of Pure 

Substance @25C, mm Hg

Specific 

Gravity

1319-77-3 Cresols 0.17 1.030-1.04

95-50-1 1,2-Dichlorobenzene 1.36 1.3

108-94-1 Cyclohexanone 4.33 0.94

110-80-5 2-Ethoxyethanol 5.31 0.93

71-36-3 n-Butanol 6.7 0.81

1330-20-7 Xylenes (total) 7.99 0.86

100-41-4 Ethyl benzene 9.6 0.87

78-83-1 Isobutyl alcohol 10.5 0.80

108-90-7 Chlorobenzene 12 1.11

127-18-4 Tetrachloroethylene 18.5 1.63

108-10-1 Methyl isobutyl ketone 19.9 0.80

79-00-5 1,1,2-Trichloroethane 23 1.435

108-88-3 Toluene 28.4 0.86

79-01-6 Trichloroethylene 69 1.47

78-93-3 Methyl ethyl ketone 90.6 0.81

141-78-6 Ethyl acetate 93.2 0.90

71-43-2 Benzene 94.8 0.88

67-56-1 Methanol 127 0.8

67-64-1 Acetone 232 0.79

75-09-2 Methylene chloride 435 1.33

Chemicals are arranged in increasing order of vapor pressure. A chemical with a higher vapor 

pressure is more volatile than a chemical with a lower vapor pressure.

To further account for solvent removal from a process such as hand wringing, a conservative value 

of 5 percent removal was used, based on data in EPA (1997). Exhibit 2-4 illustrates a sample 

calculation. This particular calculation was used to estimate the disposal quantity from one site in 

the electronics industry, and is subsequently used in Exhibit B-2 of Appendix B. As indicated in 

Exhibit B-2 of Appendix B, a total of two data points are available from the electronics industry 

(i.e., the value calculated in Exhibit 2-4 and a second value), which formed the basis of the assigned 

distribution in Appendix H.

Exhibit 2-4. Sample Calculation for Quantity of Solvent in Disposed Wipe

Parameter Value Discussion/ Basis

Quantity of solvent applied “<10 mL” per wipe as applied Table A-1, Reference Document 

No. 0118

Specific gravity of solvent 0.9 g/mL Assumed (refer to Exhibit 2-3)

Weight fraction of applied solvent which does not 

evaporate 0.78 From Exhibit B-6

Weight fraction of solvent which is not removed 

from wipe as a result of hand wringing, etc. 0.95 From text
12

Parameter Value Discussion/ Basis

Solvent remaining on wipe which reaches the 

landfill

6.7 mg per wipe = 10 mL x 

0.9 mg/mL x (0.78) x (0.95)

The various assumptions used in Exhibit 2-4 (e.g., specific gravity, evaporation rate, removal 

efficiency) reflect single point values that are within the range of actual chemical properties. In the 

case of specific gravity, the specific gravity of the evaluated solvents ranges from 0.79 to 1.63; the 

selection of 0.9 represents a value within this range. Similarly, the selected evaporation rate 

approximates the range of solvent properties. As noted before, these assumptions are used in cases 

identified in Appendix B only when the quantity of solvent on the disposed wipe was not measured 

(i.e., only when the application rate was provided). Based on Exhibit B-2, 20 data points are used in 

estimating solvent quantity loading. For nine cases, the quantity on the disposed wipe was 

estimated using the assumption methodology in Exhibit 2-4. For the remaining 11 cases, the 

quantity on the disposed solvent was measured so that the assumptions in Exhibit 2-4 were not 

required. As noted in Exhibit 2-1, the quantity of solvent applied was assumed to be independent 

of the type of solvent.

2.3.3 Percent of Wipes Containing Constituent

A facility will have one or more of the following use patterns with regard to F-listed constituents in 

solvents:

• A facility may not use an F-listed solvent at all.

• A facility may use an F-listed solvent, but may not use it in conjunction with disposable towels 

or wipes.

• A facility may use an F-listed solvent in conjunction with disposable wipes.

• A facility may use an F-listed solvent in some applications but not others.

• A facility may use combinations of the above practices.

Because of these factors, there is uncertainty with the fraction of wipes containing the constituent. 

To account and evaluate this uncertainty, two fractions were calculated using available data sources, 

forming endpoints for use in the probabilistic analysis.

The following data sources were used in developing these estimates and are discussed in detail 

below:

• 2003 Biennial Report.

• Industrial Cleaning Solvents Development Data (EPA, 1994a; ERG, 2003).

• 2004 Toxics Release Inventory (TRI) and a similar Massachusetts program, 2004 Toxics Use 

Reduction Act (TURA).

• Other sources.

The specific data used for each chemical are identified in Exhibit B-3 of Appendix B.

One of the following sources was used as the basis for one endpoint of the estimated usage range 

(listed in decreasing preference): EPA Biennial Report data, historical industrial cleaning solvents 

development data, and other sources. The Biennial Report data is a preferred data source because it 

is recent and includes many facilities.
13

The fraction of wipes containing a chemical is expected to change from one industry sector to 

another (e.g., one sector may use acetone with greater frequency than another sector). To account 

for these effects, industry-specific data are used as available.

Data Source: 2003 Biennial Report

The 2003 Biennial Report provides hazardous waste generation data for large quantity generators 

(LQG); however, many small quantity generators (SQG) also enter data voluntarily or because of 

state requirements. In part, the data show the numbers of facilities generating F001 to F005 spent 

solvents and F001 to F005 spent solid materials (which include wipes). Advantages of these data 

are their reflection of a large number of sites represented and the relatively recent collection of the 

data. Disadvantages are that specific chemicals are not identified and there is uncertainty with 

respect to whether the solvents are used in conjunction with disposable wipes.

The 2003 Biennial Report data were used directly for 10 of the most frequently used solvents 

(Exhibit B-3 identifies the data sources used for each chemical). ‘Frequently-used solvents’ include 

nine chemicals identified in NESHAP rules for targeted industries as evaluated in Appendix C (i.e., 

cresols, ethylbenzene, glycol ethers5, methanol, methylene chloride, methyl ethyl ketone (MEK), 

methyl isobutyl ketone (MIBK), toluene, and xylene). In addition, acetone (which is not a HAP) is 

included in this list because it is identified as extensively used in printing.6 A complete discussion 

of the Biennial Report data is included in Appendix D.

For these chemicals, the fraction of facilities using the chemical was assumed to be equal to the 

fraction of facilities reporting the corresponding F-listed waste. This is conservative because each 

F001 to F005 waste listing definition includes multiple chemicals. For illustration, Exhibit 2-5 

includes a sample calculation for estimating the number of printing facilities using acetone.

The data in Exhibit 2-5 reflect the sites using the chemical as a fraction of only those LQG and SQG 

generators, which generate F001-F005 solvent-contaminated wipes. This fraction is much higher 

than the fraction of the entire population of industry sites, but is consistent with the basis used in 

Sections 2.3.6 for identifying the number of generators. For example, the 85 percent value for 

acetone refers to the percentage of only those LQG and SQG sites assumed to generate F001 to 

F005 wipes (this total population is approximately 30,000, as indicated in Section 2.3.4).

Exhibit 2-5. Example Calculation for Estimating the Percent of Wipes Containing Acetone, 

Using 2003 Biennial Report Data

Parameter Source Result

Number of printing facilities 

generating F001-F005 waste

2003 Biennial Report (Exhibit 

D-4 of Appendix D) 254

Number of printing facilities 

generating F003 waste

2003 Biennial Report (Exhibit 

D-4 of Appendix D) 217

 5 Solvent use profiles identified the use of ‘glycol ethers’; 2-ethoxyethanol is a glycol ether but may or may not be part 

of the formulation. To be conservative, it is assumed to be present when the use of glycol ethers is known.

6

 Based on a survey (SGIA, 1996) of used wipe disposal practices for printers, 18 percent of facilities indicated that 

they used methyl ethyl ketone, 27 percent reported using acetone, 20 percent reported using xylene, and 20 percent 

reported using toluene.
14

Parameter Source Result

Fraction of Sites Generating F003 Calculated from above two 

data points 85%

Fraction of Sites Using Acetone 

(acetone is one of the F003 solvents)

Assume equal to F003 

generation rate 85%

Fraction of wipes at these sites which 

contain the chemical Kimberly-Clark (1997) 30% of wipes

Overall calculation for fraction of wipes containing the chemical

Of the total number of wipes used at these 

sites, approximately 26% are assumed to 

contain acetone (e.g., 85% × 30% = 26%). 

This forms one of the ‘usage endpoints’.

This calculation method was used for 10 constituents as discussed in the text.

Data Source: Industrial Cleaning Solvents Development Data

For six chemicals, frequency of use was obtained from data collected in support of EPA’s 

Alternative Control Techniques Document - Industrial Cleaning Solvents (EPA-453/R-94-015) 

(EPA, 1994a). Detailed data for cleaning solvent use by 175 sites are presented in ERG (2003). 

This information allows for identification of particular chemicals used by facilities. Limitations of 

these data include their age (1990’s) and that they do not identify use of wipes or towels. However, 

in general, cleaning solvents are likely to be at least occasionally used with wipes.

Exhibit B-3 identifies the data sources used for each chemical. Further information regarding this 

data source, including the frequency that certain chemicals were reported as used is in Section 2.3.5.

Data Source: Other Data

For four chemicals (i.e., four chemicals not discussed in relation to the TRI data below or the 

industrial cleaning solvents data above), information is available to suggest they may be used as 

solvents which potentially may contact towels and wipes. Two chemicals, cyclohexanone and 

1,1,2-trichloroethane, have been identified as used in printing.7

 Two other chemicals, 1,2-

dichlorobenzene and chlorobenzene, were detected in industrial laundry wastes (see Section 3.3.3).

Usage for the three chlorinated solvents was estimated using Document No. RCRA-2003-0004-

0106, which identifies that ‘trichloroethane’ is used in the printing industry by 2 of 63 respondents 

(3 percent) circa 1996. This percentage was applied to 1,1,2-trichloroethane for all sites, as well as 

to the two other chlorinated chemicals (1,2-dichlorobenzene and chlorobenzene) for which no other 

usage data were available.

A usage estimate for cyclohexanone is based only on the TRI/ Massachusetts TURA data below 

because no other data source is available.

 7

 According to Dave Salman, EPA (Salman, 2006), cyclohexanone is used in some digital printing; the 1993 Printing 

Ink Manual published by the Society of British Printing Ink Manufacturers identifies cyclohexanone as "used in special 

screen inks". Cyclohexanone is also identified as used in the aerospace industry (Document No. RCRA-2003-0004-

0119; Appendix A). Appendix C to EPA PrintStep Printers' Plain Language Workbook Template (Printstep, 1999) 

identifies 1,1,2-trichloroethane as potentially used in some printing processes. 
15

Data Source: TRI and TURA

To account for uncertainty with chemical use, data obtained from 2004 TRI and a similar 

Massachusetts program, 2004 TURA, were used as the basis for another end of the assumed range 

of use (Exhibit B-3 identifies the data sources used for each chemical). Most of the F-listed 

constituents are identified in the 2004 TRI or 2004 TURA toxic chemical reporting results. These 

results, in part, show the frequency that certain chemicals are reported by facilities. While the 

results do not identify whether the chemical is used as a solvent in conjunction with towels or 

wipes, or even if it is used as a solvent at all, the data show the frequency that the chemical is 

reported as present above the threshold reporting quantities.

Because of its nationwide scope, data from TRI were used preferentially over the Massachusetts 

TURA data. The Massachusetts TURA data were used for those constituents, such as acetone, that 

are not included in the list of chemicals for which TRI reporting is required. See Exhibit E-3 in 

Appendix E for the TRI and TURA data.

Resulting Use of Data

Each of these sources is used to identify or estimate the percentage of facilities using a particular 

type of chemical. However, not all wipes used at a site will be used in conjunction with F-listed 

solvents, and a site may use multiple types of solvents. Kimberly-Clark (1997) has estimated that 

30 percent of disposables generated by a facility contacts ‘hazardous solvents’ (this percentage 

applies to sites in which wipes contact ‘hazardous fluids’ at any amount). This 30 percent value is 

used in the calculation to estimate the fraction of wipes that contain a particular chemical.8

Values used in the analysis for the printing sector are summarized in Exhibit 2-6 (values for this 

sector are shown as an example). Data for other industries are provided in Exhibit B-3 in Appendix 

B.

Exhibit 2-6. Data Used for Estimating the Percent of Wipes Generated by the Printing Sector 

Containing Certain Constituents

Chemical FListing

Range / Average 

for Printing 

Sector

Are IndustryDependent Data 

Used for Other 

Sectors?

Data Sources Used 

(see table note)

Min Max

Acetone F003 16-26% (avg. 21%) Yes (Biennial Report) 5 1

Benzene F005 0.2-2.1% (avg. 1.1%) No – same for all 2 6

n-Butanol F003 2.1-5.4% (avg. 3.7%) No – same for all 2 6

Chlorobenzene F002 0.4-0.9% (avg. 0.6%) No – same for all 6 3

Cresols F004 0-0.3% (avg. 0.2%) Yes (Biennial Report) 1 6

Cyclohexanone (note 4) F003 1.8% No – same for all 5 5

1,2-Dichlorobenzene F002 0.2-0.9% (avg. 0.5%) No – same for all 6 3

Ethyl acetate F003 2.4-6.6% (avg. 4.5%) No – same for all 2 5

Ethyl benzene F003 5.7-26% (avg. 16%) Yes (Biennial Report) 6 1

2-Ethoxyethanol F005 0.1-20% (avg. 10%) Yes (Biennial Report) 6 1

Isobutyl alcohol F005 0.3-0.9% (avg. 0.6%) No – same for all 2 5

 8

 The Alliance of Automobile Manufacturers (AAM, 2006) similarly estimates that not all wipes at a facility are used 

with F-listed solvents. AAM estimates five to ten percent of wipes at its facilities contain F-listed solvents. As a 

conservative assumption, and because the Kimberly-Clark estimate is based on a variety of industries, the calculation 

uses the higher Kimberly-Clark estimate of 30 percent.
16

Chemical FListing

Range / Average 

for Printing 

Sector

Are IndustryDependent Data 

Used for Other 

Sectors?

Data Sources Used 

(see table note)

Min Max

Methanol F003 10-26% (avg. 18%) Yes (Biennial Report) 6 1

Methyl ethyl ketone F005 8.4-20% (avg. 14%) Yes (Biennial Report) 6 1

Methyl isobutyl ketone F003 4.2-26% (avg. 15%) Yes (Biennial Report) 6 1

Methylene chloride F001, 

F002 2.1-3.5% (avg. 2.8%) Yes (Biennial Report) 6 1

Tetrachloroethylene F001, 

F002 0.7-0.9% (avg. 0.8%) No – same for all 2 6

Toluene F005 13-20% (avg. 16%) Yes (Biennial Report) 6 1

1,1,2-Trichloroethane F002 0.1-0.9% (avg. 0.5%) No – same for all 6 3

Trichloroethylene F001, 

F002 1.7-1.8% (avg. 1.8%) No – same for all 2 6

Xylene F003 12-26% (avg. 19%) Yes (Biennial Report) 6 1

1. 2003 Biennial Report. Assume chemical use (e.g., acetone) in the industry is equal to overall use of corresponding Flisted solvent (e.g., F003). Different usage percentages are used for different industries.

2. Miscellaneous Metal Parts NESHAP. The same percentages are used for all industries due to lack of data to 

differentiate.

3. Chlorinated solvent usage assumption for printing industry (Document No. RCRA-2004-0004-0106). This same 

percentage is used for all industries due to lack of data to differentiate.

4. Except as indicated, no other data are available for cyclohexanone to form a basis of estimate.

5. 2004 TURA. The same percentages are used for all industries.

6. 2004 TRI. The same percentages are used for all industries.

2.3.4 Estimated Count of Spent Solvent Disposable Wipes Generators

This term is intended to reflect the number of facilities using disposable wipes in conjunction with 

solvents. The population is limited to RCRA LQG and SQG facilities (smaller CESQG facilities 

are already exempt from RCRA regulations for disposable wipes management) and RCRA solvents. 

As a result of the methodology described in this section, a single point estimate of the number of 

generators is developed. There is uncertainty associated with this point estimate because of 

uncertainties in the contributing data terms. The overall methodology used for developing the point 

estimate is presented below. Each of the contributing terms, as well as the associated uncertainties, 

is described in the following text.

• Step 1: Estimate the number of affected facilities in a subset of defined sectors.

(a) There are 445,010 total facilities in 13 evaluated sub-sectors.

(b) 33.5 percent of the facilities use RCRA solvents on their wipes;

(c) 67.1 percent of the facilities are either a RCRA LQG or SQG regulated facility 

(d) 8.1 percent of the facilities use primarily disposable wipes rather than reusable wipes.

• Step 2: Expand the estimate from the defined sectors to across all industries. Of sites impacted, 

56 percent are estimated to be included in the 13 sub-sectors evaluated above.

• Step 3: Account for the potential increased use of disposables in the future, if a regulatory 

exclusion is implemented. In an earlier economic analysis prepared by EPA (2003), disposable 

use may increase up to 17%.

• The resulting number of generators is estimated from the above data, as illustrated in Exhibit 2-

7.
17

Exhibit 2-7. Sample Calculation for Estimating Number of Generators of Disposable Wipes

Value Parameter

 445,010 Facilities in 13 evaluated sub-sectors.

x 33.5% % Facilities using RCRA solvents on their wipes.

x 67.1% % Facilities which are either a RCRA LQG or SQG (i.e., subject to 

RCRA hazardous waste management regulations).

x 8.1% % Facilities using primarily disposable wipes rather than reusable 

wipes.

/ 0.56 ‘Scale up’ from the 13 evaluated sectors to all sectors.

x 17%/8.1% Potential future impact of increased use of disposable wipes in 

future.

= 30,380 facilities Result differs slightly from expression due to rounding.

The resulting estimate of 30,380 facilities is used in subsequent calculations, and is intended to 

represent a ‘most likely estimate’ of this parameter. It is intended to take into account facilities 

from all industries using disposable wipes, as well as potential increased usage in the future. A 

number of different data sources were used to develop this estimate, which are described below.

Step 1: Estimate the Number of Affected Facilities in a Subset of Defined Sectors

As a starting point, EPA’s July 2003 Economic Impact Analysis for the November 2003 wipes 

proposed rule (EPA, 2003) was used in identifying the number of potentially impacted generators 

(i.e., generator facilities) of spent solvent disposable wipes in each of 13 evaluated industrial subsectors. Based on the framework of the July 2003 economic analysis (EPA, 2003), EPA developed 

for the purpose of these landfill loading calculations an updated estimate of 8,106 facilities (EPA, 

2006a); this update resulted from using the more recent 2002 Economic Census data 

(http://www.census.gov/econ/census02) in place of the 1997 Economic Census data used in EPA 

(2003). The updated wipes generator count reflects three filtering (screening) assumptions applied 

in the economic analysis to the respective Economic Census counts of industrial facilities:

1) 33 percent of the 445,010 total facilities in these 13 sub-sectors (source: 2002 Economic 

Census) use RCRA solvents on their wipes;

2) of which 67 percent are either a RCRA LQG or SQG regulated facility excluding CESQGs 

which are already exempt from RCRA regulations; 

3) of which 8 percent use primarily disposable wipes rather than reusable wipes.

This updated estimate is 8,106 sites (i.e., 445,010 x 33.5% x 67.1% x 8.1% = 8,106). These 

filtering percentages are overall average values from EPA (2003). The sources and computations 

involving these facility count filtering percentages are provided in EPA's 2003 Economic Analysis 

for the proposed rule (EPA, 2003).

Step 2: Expand the Estimate from the Defined Sectors to Across All Industries

A potential limitation of the estimated count from EPA (2006a) is that not all industries using 

solvent wipes may be accounted for (because more than these 13 industries may use disposable 

wipes), and therefore the number of facilities and wipe usage may be underestimated in EPA's 2003 

economic analysis. Data from EPA’s 2003 Biennial Report were used to identify and account for 

these possible additional facilities in additional industries.
18

The following methodology provides additional detail regarding how the 2003 Biennial Report data 

were used in developing the estimated number of wipe generators:

• Results from the WR Form of the 2003 Biennial Report were used for estimating the number of 

generators. This form is completed by companies receiving hazardous waste for management 

(e.g., Treatment, Storage, and Disposal (TSD) facilities); these companies report the sites from 

whom waste was received (e.g., generators). According to these data, F001-F005 hazardous 

waste was received from 63,845 facilities in 2003. These were assumed to be hazardous waste 

generators, but are of all sizes (i.e., LQGs, SQGs, and CESQGs) and include all forms of F001-

F005 wastes, not just wipes.

• NAICS codes were obtained for these generators from EPA’s RCRA Info database. NAICS 

codes were not available for a significant number of sites (32,382 of 63,845, or 51%). This high 

percentage of missing data could be because: (a) there are typographic errors between the 

Biennial Report data and the RCRA Info data used in looking up the NAICS codes; (b) the 

generator identified by the TSD is a CESQG and not included in RCRA Info; or (c) the facility 

was in RCRA Info but no NAICS data were available for the facility.

• Approximately 56 percent of the facilities reported one or more NAICS codes ‘covered’ as the 

originally evaluated 13 industries, which means that approximately 44 percent are not. This 

does not include the significant number of facilities with blank NAICS codes. Facilities can 

report more than one NAICS code. This percentage (56 percent) is applied above.

Additional information was used to assess the reasonableness of the estimated present- day number 

of wipes generators (from above equation, this is equivalent to 8,106/0.56 = 14,475 sites). Results 

are summarized below:

• EPA evaluated available data to identify ten industries, in addition to the 13 previously 

evaluated, which may use RCRA-regulated solvent wipes. Details are in Appendix G. 

Applying the same filtering (screening) percentages as summarized above for the initial 13 

industrial sub-sectors (e.g., 33.5% of facilities use RCRA solvents on wipes), provides an 

estimate of 6,920 additional RCRA-regulated spent solvent disposable wipes generators. When 

added to the 8,106 count for the 13 sub-sectors, the resulting estimate is 15,026 (6,920 + 8,106) 

generators of spent solvent disposable wipes. 

There is uncertainty with this estimate because the percentage of RCRA solvent use, wipe use, 

etc. in these additional industries was assumed to be the same as those for the 13 sectors initially 

evaluated. EPA does not have wipes-related information for these other 10 candidate industries. 

Nevertheless, the resulting estimate using this method (15,026 sites) is very close to the estimate 

using the Biennial Report data (14,475 sites).

• The number of sites calculated using the above method (14,475 sites) was compared to several 

other estimates derived from the Biennial Report data:

o As noted above, according to the WR Form of the 2003 Biennial Report, 63,845 facilities 

generate F001-F005 wastes in 2003. These are LQGs, SQGs, and CESQGs and include all 

forms of these wastes, not just wipes.
19

o Data from the GM Form (generally these are LQGs) show that 9,877 facilities generated 

F001-F005 wastes in 2003. This includes wastes other than shop towels and wipes. Refer 

to Exhibit D-2 in Appendix D.

As should be expected, the estimated number of (present-day) facilities of 14,475 is lower than 

the estimated 63,845 LQGs, SQGs, and CESQGs generating F001-F005 wastes.

Step 3: Account for the Potential Increased Use of Disposables in the Future

In addition to estimating potential direct impacts of the November 2003 industrial wipes proposed 

rule, EPA’s July 2003 Economic Impact Analysis (EPA 2003) estimated the potential “induced 

impacts” of the proposed exclusions in the form of potential effects on relative prices and national 

market share of disposable and reusable wipes. Specifically, this affects the assumption of the 

percentage of facilities primarily using disposable wipes. Precise forecasts affecting the degree of 

this change in market share are dependent, in part, on the regulatory option selected for the final 

rule (which does not exist at the time of this writing). As an approximation, the forecasts used in 

the Economic Analysis for the proposed rule (page 37 of EPA, 2003) will be used where the relative 

national market share for disposable wipes increases to 17 percent (for a ‘most likely’ scenario).

The following calculation shows the estimated number of facilities using disposable wipes that is 

applied in this landfill loading analysis:

8,106 Updated estimate of the number of sites in the initial 13 industrial sub-sectors 

identified as using disposable wipes, from EPA (2006a).

/ 0.56 Factor developed from a query of EPA’s 2003 RCRA Biennial Report data 

(as explained below), to expand the estimated number of facilities to include 

other industries not identified in EPA (2003).

x (17%/8.1%) Potential “induced impacts” multiplier to increase the relative market share of 

disposable wipes (based on EPA, 2003) which projects a net nationwide 

increase in the use of disposable wipes to 17% of facilities as a result of the 

proposed regulatory exclusion. 9

30,380 Resultant count of industrial facilities estimated to use disposable wipes 

following a final exclusion, projected for all industries.

These data are summarized in Exhibit 2-8. The facility counts from the Economic Analysis, used in 

deriving the data in Exhibit 2-8, are reproduced in Appendix B (Exhibit B-3). The number of sites 

in each sector listed in Exhibit 2-8 is derived by multiplying the percentages (in Exhibit B-4 of 

Appendix B and obtained from the Economic Analysis data) by the 'scaled up' total of 30,380 sites.

Exhibit 2-8. Percent of Generators in Each Sector

Sector EA Sector # Number of Sites Percent of Total

Printing 1, 10, 11 7,262 24%

 9

 The percent of facilities estimated to use disposables in the future is estimated to be 17% using EPA (2003). This 

new value is used to essentially replace the previously used ‘present day’ value of 8.1% noted earlier. 
20

Sector EA Sector # Number of Sites Percent of Total

Transportation 7 334 1%

Auto repair 12 4,252 14%

Electronics 6 147 0.5%

Other 2, 3, 4, 5, 8, 9, 13 5,018 61% All Other --- 13,367

Total 30,380 100%

EA Sector number: Numbering convention as used in the Economic Analysis (EPA, 2003).

Sum of numbers may differ very slightly from the total due to rounding.

2.3.5 Percent of Constituent in the Solvent

An F-listed chemical may be present in a solvent in a 99+ percent pure form, or may be present in a 

trace amount. For example, benzene is often present only as a trace ingredient of petroleum-based 

solvents. The percent of constituent present in a solvent is assumed different for each chemical. 

Data for these formulations are obtained from development data used for the Miscellaneous Metal 

Part NESHAP (ERG, 2003). This source identifies the cleaning solvents used at a large number of 

sites (175), but the source is somewhat dated (from the early 1990’s). Data were available for 16 

chemicals and are included in Exhibit 2-9. For four other evaluated chemicals not identified in 

ERG (2003) (i.e., chlorobenzene, cyclohexanone, 1,2-dichlorobenzene, and 1,1,2-trichloroethane), a 

range of 1 to 100% was assumed (in the probabilistic analysis, the concentration is assumed to vary 

uniformly over this range). This step of the calculations only accounts for chemicals present in a 

solvent at non-zero concentrations, so the lower bound of the range for this parameter is not zero for 

any chemical.

Exhibit 2-9. Source of Data Used for Percent of Constituent in Solvent

Chemical Data Source % of 

Sites A

Number 

of Sites

Distributional 

Assumption

1,1,1-Trichloroethane B NESHAP (ERG, 2000) 1% 2 ---

Acetone NESHAP (ERG, 2000) 10% 17 Exponential

Benzene NESHAP (ERG, 2000) 1% 1 Constant

Chlorobenzene Assumption --- --- Uniform

Cresols NESHAP (ERG, 2000) 1% 2 Uniform

Cyclohexanone Assumption --- --- Uniform

1,2-Dichlorobenzene Assumption --- --- Uniform

Ethyl acetate NESHAP (ERG, 2000) 8% 14 Lognormal

Ethyl benzene NESHAP (ERG, 2000) 16% 28 Exponential

2-Ethoxy ethanol / 

Glycol ethers C NESHAP (ERG, 2000) 7% 12 Lognormal

Isobutanol NESHAP (ERG, 2000) 1% 2 Uniform

Methanol NESHAP (ERG, 2000) 15% 26 Lognormal

Methyl ethyl ketone NESHAP (ERG, 2000) 43% 75 Beta

Methylene chloride NESHAP (ERG, 2000) 6% 10 Weibull

MIBK NESHAP (ERG, 2000) 18% 32 Weibull

n-Butanol NESHAP (ERG, 2000) 7% 12 Lognormal

Tetrachloroethylene NESHAP (ERG, 2000) 2% 4 Weibull

Toluene NESHAP (ERG, 2000) 31% 55 Extreme Value

1,1,2-Trichloroethane Assumption --- --- Uniform

Trichloroethylene NESHAP (ERG, 2000) 6% 10 Weibull

Xylene NESHAP (ERG, 2000) 38% 66 Exponential

ERG, 2003 is from Miscellaneous Metal Parts NESHAP Development. The 2003 ERG memo identifies that 175 

facilities reported using cleaning solvents. The memo lists the solvents used by each of these 175 facilities.
21

A. Percent is of the 175 sites reporting cleaning solvent use (ERG, 2003); these data are used, in part, in estimating the 

percent of wipes containing some chemicals as identified in Section 2.3.3.

B. ERG (2003) includes data for 1,1,1-trichloroethane; as noted in Section 1.2.1, landfill loading estimates are not 

developed for this chemical.

C. Data for glycol ethers in ERG (2003) were used for 2-ethoxy ethanol.

Exhibit 2-9 represents a summary of the data used in the evaluation. The columns ‘% of Sites’ and 

‘Number of Sites’ identifies the number of data points used for developing the input parameter 

distribution assumption (i.e., one data point for each site). For example, for methanol, 26 data 

points were used to generate the input parameter distributional assumption using a best fit of the 

data. Much less data were available for isobutanol, where only two data points were available.

2.3.6 Number of Generators Disposing Towels or Wipes in a Single Landfill

As identified in Section 2.3.4, approximately 30,000 generators are assumed to generate F001 to 

F005 solvent-contaminated disposable wipes; these generators are assumed to dispose their spent 

wipes as a nonhazardous waste. Nationwide, there are approximately 1,600 MSW landfills. 

Therefore, the analysis accounts for the use of one landfill by multiple generators. Neither the 

landfills nor the generators are spread uniformly across the country; the following factors were 

assumed to affect this distribution:

• The number of generators per state. This is approximated by using Department of Census state 

distribution data for six target industries (Census, 2002), and applying this distribution to the 

approximately 30,000 generators to estimate the number of generators per state.

• The number of landfills per state (using Simmons, 2006 data). For example, there are a lower 

number of landfills in Northeastern states as compared to the rest of the country.

• For each state, the percentage of MSW, which is landfilled as opposed to being combusted 

(using Simmons, 2006 data). Wipe disposal is assumed to follow this percentage. For example, 

Northeastern states rely on combustion more so than Midwestern states, and therefore 

Northeastern states were assumed to dispose less of their wipes into landfills.

The average number of generators per landfill was calculated for each state using the above data 

(with a minimum of one generator per landfill). This distribution (which ranges from 2 to 67 

generators per landfill) was used as part of the analysis; the data are included as Exhibit B-5 in 

Appendix B. As indicated in Exhibit 2-1, the empirical data were ‘best fit’ to a probability 

distribution.

2.4 Results

2.4.1 Loading Estimates

The above data were used as inputs for each of 20 chemicals (i.e., 10 chemicals identified in Section 

1.2 were not assessed). The calculations were conducted using Microsoft Excel®. Calculations 

were conducted using both deterministic (point-value) and Monte Carlo methods. The deterministic 

calculation was conducted by setting all inputs to their average values.
22

The Monte Carlo analysis was conducted with Excel® spreadsheets using Crystal Ball®. As 

discussed above, either a distribution or a fixed value is used for each input parameter (based on 

data availability). A separate calculation is conducted for each of the 20 chemicals. Certain input 

parameters are not affected by the evaluated chemical (e.g., number of towels used by a generator), 

and are assessed using the same probability distribution for all chemicals. For each of 100,000 

iterations, all parameters are assessed independently. The output of a Monte Carlo analysis results 

in a distribution of values. A sample graphical Monte Carlo output is presented in Exhibit 2-10.

Exhibit 2-10. Monte Carlo Results for Acetone in Disposable Wipes

The values in Exhibit 2-11 represent landfill loading estimates for each chemical (i.e., the quantity 

of chemical that a single landfill will receive from large and small quantity generators of hazardous 

waste). The Monte Carlo analysis provides percentile results from 0 to 100; for summary purposes 

the mean, median (50th percentile), 90th percentile, and 95th percentile values are shown in Exhibit 

2-11. The deterministic value, assessed by setting all input parameters to their mean values, is also 

presented (the sample calculation for this deterministic result was previously presented in Exhibit 2-

1).

One use of the deterministic results is to provide a reference in reviewing the Monte Carlo results. 

For each chemical, the deterministic result is between the 50th and 90th percentiles of the Monte 

Carlo distribution, and close to the mean of the Monte Carlo result. For example, Exhibit 2-11 

identifies a deterministic result for acetone of 4,966 kg/landfill/year, a mean Monte Carlo result of 

4,041 kg/landfill/year, a 50th percentile Monte Carlo result of 1,033 kg/landfill/year, and a 90th

percentile Monte Carlo result of 9,120 kg/site/year.

The deterministic result (using mean values as input) and the mean of the probabilistic result are 

very close, indicating good consistency. The reason that the deterministic results are generally 

higher than the 50th percentile Monte Carlo results is due to the shape of the input distributions (e.g., 

lognormal distributions); a characteristic of this assumption is that the average value is higher than 

the median value. This is shown in Exhibit 2-12 for several input parameters.

Frequency Chart

 kg/yr

.000

.075

.151

.226

.301

0

30128

0 10,000 20,000 30,000 40,000

100,000 Trials 1,311 Outliers

Forecast: Disposables (kg/yr) - Acetone
23

Exhibit 2-11. Annual Landfill Loading Results for Disposable Wipes

Constituent Result per Landfill (kg/yr)

Deterministic 

(Mean Inputs)

Probabilistic

Mean 50th

Percentile

90th

Percentile

95th

Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane 

Ethyl ether

Nitrobenzene

2-Nitropropane

Pyridine

1,1,1-Trichloroethane

1,1,2-Trichlorotrifluoroethane

Trichlorofluoromethane

0 0 0 0 0

Acetone 4,966 4,041 1,033 9,120 16,224

Benzene 4.7 5.4 1.8 12.1 20.2

n-Butanol 298 291 61 607 1,146

Chlorobenzene 164 193 56 431 752

Cresols 28 29 9 58 103

Cyclohexanone 493 549 159 1,207 2,102

1,2-Dichlorobenzene 137 162 46 364 631

Ethyl acetate 172 192 52 414 733

Ethyl benzene 774 861 184 1,857 3,446

2-Ethoxyethanol 2,083 1,242 222 2,606 4,974

Isobutyl alcohol 90 98 31 220 376

Methanol 1,161 1,208 225 2,431 4,747

Methyl ethyl ketone 5,140 5,052 1,307 11,676 20,532

Methyl isobutyl ketone 1,129 1,015 150 2,073 4,184

Methylene chloride 1,441 1,505 465 3,268 5,583

Tetrachloroethylene 353 323 107 727 1,226

Toluene 3,932 3,920 1,215 8,918 15,180

1,1,2-Trichloroethane 137 149 40 330 581

Trichloroethylene 900 842 308 1,911 3,146

Xylenes (total) 5,106 3,835 958 8,556 15,184

Deterministic result is calculated using the mean (average) value of all input parameters.

For 10 chemicals, no use was assumed; therefore, results are zero.

Exhibit 2-12. Comparison of Average and Median Values for Input Parameters 

Parameter Average Value Median Value

Number of disposables used per site per year (printing) 129,990 41,350

Number of disposables used per site per year (transportation) 73,819 37,500

Number of disposables used per site per year (auto repair) 12,964 7,692

Number of disposables used per site per year (other) 66,436 15,500

Number of generators/landfill 18.9 16

Median input values are from the raw empirical data, and not from the parameters of the fitted distribution.

2.4.2 Sensitivity Analysis

Several techniques were used to identify which input parameters contribute most to variation in risk 

estimates. As reflected in Exhibit 2-1, landfill loading results for a single chemical requires 21 

input parameters. Sensitivity analyses were conducted for those input parameters where sufficient 

information is available to identify a distribution or range of values. 
24

Inspection of Exhibit 2-1 shows that the percent of generators in each sector (e.g., printers) is 

assumed to be constant. The percentage of generators is highest for ‘other industries’ and ‘printers’, 

and lowest for electronics and transportation. These percentages affect the sensitivity of industryspecific parameters (such as number of wipes used per site per year in each sector). As a result, 

input parameters which affect the printing and ‘other’ sectors are much more likely to impact the 

overall result than input parameters which only affect the remaining three sectors. 

To further evaluate sensitivity, rank correlation coefficients for each parameter were calculated 

using CrystalBall® software. Further information on this method is provided in Appendix A to 

EPA’s Risk Assessment Guidance for Superfund Volume III Part A, Process for Conducting 

Probabilistic Risk Assessment (EPA 2001). In this method, variations in each input parameter are 

correlated to the output. The higher the correlation, the higher is the dependence of the particular

input parameter on the result (the landfill loading).

The results, shown in Exhibit 2-13, are expressed as the square of the rank correlation coefficients 

and normalized to 100% (i.e., the results in this column add to 100%). The results serve two 

purposes. First, the most sensitive parameter is indicated with the highest sensitivity result. 

Second, the results provide an indication of relative sensitivity. Results that are close to one another 

(e.g., the input parameters identified in the top two rows) indicate that one parameter is only slightly 

more sensitive than the other.

Exhibit 2-13 presents the parameters evaluated, the result for one chemical (acetone) arranged from 

most sensitive to least sensitive, and discussion of results for all 20 chemicals. The most sensitive 

parameter is indicated with the highest sensitivity result. Exhibit 2-13 shows that the number of 

disposable wipes used per year in printing and the number of disposable wipes used per year in 

other industries are the two most sensitive parameters for the majority of chemicals evaluated and 

can be considered the most important with respect to their influence on variability of results. From 

Exhibit 2-13, the most sensitive parameters are as follows:

• Number of wipes used per site per year, in printing and ‘other’

• Solvent loading per wipe, in printing and ‘other’

• Percent of chemical in solvent

• Number of generators per landfill

The following are limitations to this sensitivity analysis:

• Sensitivity was not evaluated for ‘constant’ parameters such as the percent of generators in each 

sector (e.g., printers), which is directly related to the number of sites in the sector. This does not 

imply that the result is insensitive to the input parameter. Estimates for these parameters were 

primarily developed from Census data (described in Section 2.3.4) in which variability was not 

assessed, and insufficient information is available to develop a plausible distribution. 

Nevertheless, the estimate used in the analyses (i.e., that printer sites make up the largest single 

sector, from Exhibit 2-8) is supported by observations that the printing sector has historically 

been a principal stakeholder in EPA’s solvent wipes activities.

• Data limitations affect the results of the sensitivity analysis, when the range of a given 

parameter is not fully characterized. For example, the fraction of wipes containing the chemical 
25

is estimated over a fairly limited range for most parameters (described in Section 2.3.3) from 

two data points. Again, insufficient information is available to develop a more extensive 

distribution, and instead the input parameter was assumed to vary uniformly between these two 

points.

Exhibit 2-13. Sensitivity Analysis for Disposables Evaluation

Parameter Result for Acetone Result for All 20 Chemicals

Percent constituent in solvent 41.1% This parameter was the most sensitive for 14 of 19 

chemicals.

Number of disposable wipes 

used per site per year in Other 

Industries 

18.1%

This parameter was the second-most sensitive for 

13 of the 20 chemicals; for six of the remaining 

chemicals it was the most sensitive parameter.

Number of disposable wipes 

used per site per year in Printing 16.4% For all 20 chemicals this was the third- or fourthmost sensitive parameter.

Number of generators per 

landfill 16.1%

For 13 of 20 chemicals this was the third- or 

fourth-most sensitive parameter; for the remaining 

seven chemicals it was the second-most sensitive 

parameter.

Solvent Loading, g/wipe in 

Other Industries 5.4% For 17 of 20 chemicals this was the fifth-most 

sensitive parameter.

Solvent Loading, g/wipe in 

Printing 2.5% For 17 of 20 chemicals this was the sixth-most sensitive parameter.

Percent wipes containing 

chemical in Printing 0.1%

These parameters are the least sensitive. They are 

less sensitive than the six parameters above for 17 

of 20 chemicals.

Percent wipes containing 

chemical in Other Industries 0.1%

Percent wipes containing 

chemical in Transportation 0.0%

Percent wipes containing 

chemical in Auto Repair 0.0%

Percent wipes containing 

chemical in Electronics 0.0%

Solvent Loading, g/wipe in 

Transportation 0.0%

Solvent Loading, g/wipe in Auto 

Repair 0.0%

Solvent Loading, g/wipe in 

Electronics 0.0%

Number of disposable wipes 

used per site per year in 

Electronics

0.0%

Number of disposable wipes 

used per site per year in 

Transportation 

0.0%

Number of disposable wipes 

used per site per year in Auto 

Repair

0.0%

The sensitivity results for each parameter are in units of ‘percent’ and total to 100%.
26

2.4.3 Effects of Input Parameter Distribution Assumptions on Results

The results presented in Section 2.4.1 are based, in part, on assumptions made with regard to the 

distribution of each input parameter. To assess the significance of changes in the input parameter

distribution assumptions on the results, several ‘what-if’ analyses are shown in this section.

Procedure

As described in Section 1.3, most of the assumptions were selected using a ‘goodness of fit’ test: the 

assumption with the best fit was selected. The purpose of this section is to describe the impact on 

results when changing these assumptions. The following conditions are used in this section as a 

basis for selecting a different distribution assumption for an input parameter:

• When two or more distributions provide a ‘good fit’ to the data. As discussed in Section 1.3, 

generally the assumption providing the best fit is used in the analysis because there is typically 

no information to assess which distribution assumption would be most appropriate for the 

population from a purely mechanistic perspective. This section examines the impact that an 

alternative assumption would have on the results. Appendix H summarizes the results for six 

input parameters and includes graphs comparing the selected distributions to the empirical data.

• When data outliers may exist. Potential outliers in the data inputs are identified in this section 

and an alternative distribution, without the outlier, is developed. When an outlier exists, it does 

not mean it should necessarily be removed, because it may be representative of a tail of a 

population. Instead, the purpose of this analysis is to evaluate the effects that such an 

assumption may have on the results. This evaluation is conducted, in part, to investigate 

impacts of uncertainties in the underlying data. Appendix H summarizes the outlier evaluation 

results for three input parameters.

• To adjust for unrealistic simulations. A 2010 public comment by INDA identified that it would 

be appropriate to include an upper limit on the amount of solvent present on a wipe, because 

under all possible exclusion scenarios a wipe would be required to contain no free liquids. As 

an alternative assumption, all distributions of the amount of solvent on a wipe (described in 

Section 2.3.2) are truncated at 100 grams per wipe; this value was chosen as an upper bound 

because no observations were above this value.10 Specifically, the input distributions for the 

following three parameters were adjusted to truncate at 100 grams per wipe: solvent loading for 

printing, solvent loading for transportation, solvent loading for other industries. All three of 

these parameters use a Weibull distribution (i.e., an unbounded probability distribution). Input 

distributions for two other solvent loading parameters, auto repair and electronics, did not 

require adjustment because they were bounded probability distributions with the maximum 

value less than 100 grams per wipe.

• In this section, most or all analyses are limited to those input parameters identified as most 

sensitive in Section 2.4.2:

o Number of wipes used per site per year, in printing

o Number of wipes used per site per year, in ‘other’

 10 Some judgment is used here, due to limitations in source data, with regard to the precise value to use as a plausible 

upper bound. It is intended to reflect ‘plausible’ and not ‘absolute’ conditions.
27

o Solvent loading per wipe, in printing

o Solvent loading per wipe, in ‘other’

o Fraction of chemical in solvent

o Number of generators per landfill

Results

In these analyses, the assumption regarding the shape of the input distributions is changed, one at a 

time, for several input parameters. This analysis does not include all possible combinations of the 

different assumptions. The procedure and results are shown in Exhibits 2-14 through 2-16.

Exhibit 2-14. Effect of Changes in Input Parameter Distributions on Results for Methylene 

Chloride

Parameter

How was 

Distribution 

Changed?

Reason for Change in 

Assumption

Result, 50th

Percentile

Result, 90th

Percentile

Result, 95th

Percentile

“Baseline” No change No change 465 3,268 5,583

Number of wipes 

used per site per 

year, in printing

From gamma to 

lognormal Per Exhibit H-1 in Appendix 

H, both gamma and lognormal 

distributions provide good fits 

to the data.

459 3,673 6,794

Number of wipes 

used per site per 

year, in ‘other’

From lognormal 

to gamma 473 3,348 5,527

Number of wipes 

used per site per 

year, in ‘other’

Eliminate 

potential outlier; 

re-evaluate with 

lognormal

Per Exhibit H-2, the maximum 

value is a potential outlier. A 

lognormal distribution is a 

good fit for the new data set.

429 2,837 4,806

Solvent loading per 

wipe, in printing

From Weibull to 

normal Per Exhibit H-1, the normal 

distribution also provides a 

reasonable fit to the data.

485 3,289 5,632

Solvent loading per 

wipe, in ‘other’

From Weibull to 

normal 469 3,247 5,621

Number of 

generators per 

landfill

From gamma to 

lognormal

The distribution is described 

almost equally well as a 

gamma distribution or a 

lognormal distribution; per 

Exhibit H-1, both provide good 

fits.

458 3,230 5,718

Number of 

generators per 

landfill

Eliminate 

potential outlier; 

re-evaluate with 

gamma

Per Exhibit H-2, the maximum 

value is a potential outlier. A 

lognormal distribution is a 

good fit for the new data set.

451 3,081 5,217

Results for ‘baseline’ case are identical to those in Exhibit 2-11.
28

Exhibit 2-15. Effect of Changes in Concentration Input Parameter Distributions

Parameter

How was 

Distribution 

Changed?

Reason for Change Result, 50th

Percentile

Result, 90th

Percentile

Result, 95th

Percentile

“Baseline” for 2-

ethoxy ethanol No change No change. 222 2,606 4,974

2-ethoxy ethanol Lognormal to 

Normal

Per Exhibits H-16 and H-28, 

both provide good fits. 701 5,915 10,583

“Baseline” for 

acetone No change No change. 1,033 9,120 16,224

Acetone Lognormal to 

Normal

Per Exhibits H-13 and H-27, 

both provide good fits. 1,750 12,955 22,001

“Baseline” for 

methylene chloride No change No change. 465 3,268 5,583

Methylene chloride Weibull to 

Normal

Per Exhibits H-19 and H-26, 

both provide good fits. 428 3,023 5,223

Results for ‘baseline’ case are identical to those in Exhibit 2-11.

Exhibit 2-16. Effect of Changes in Solvent Loading Input Parameter Distributions

Parameter

How was 

Distribution 

Changed?

Reason for Change Result, 50th

Percentile

Result, 90th

Percentile

Result, 95th

Percentile

“Baseline” for 

acetone No change No change. 1,033 9,120 16,224

Acetone

Truncated upper 

end of solvent 

loading at 100 

gram/wipe

Response to 2010 INDA 

public comment [wipes with 

too much solvent would not 

pass free liquid test and could 

not be disposed in landfill].

973 8,527 15,099

“Baseline” for 

tetrachloroethylene No change No change. 107 727 1,226

Tetrachloroethylene

Truncated upper 

end of solvent 

loading at 100 

gram/wipe

Response to 2010 INDA 

public comment [wipes with 

too much solvent would not 

pass free liquid test and could 

not be disposed in landfill].

102 670 1,117

Results for ‘baseline’ case are identical to those in Exhibit 2-11.

Conclusions

The most significant effects on results are in Exhibit 2-15, where changes in input parameter 

assumptions on concentrations are made. This is consistent with the sensitivity analyses in Section 

2.4.2, where concentration was typically the most sensitive parameter. In Exhibit 2-14, variation in 

the results is less pronounced, indicating that changes in these input parameter assumptions have 

minor effects on results.

Exhibit 2-16 shows that the inclusion of a plausible upper bound of 100 grams per wipe had little 

effect on results. As expected, this change resulted in lower loadings than the original analysis 

using unbounded input distributions. The magnitude of this difference was approximately 6 to 7 

percent for acetone, and approximately 8 to 9 percent for tetrachloroethylene, at the 90th and 95th

percentiles. Differences at the 50th percentile were less.
29

The results presented in this section demonstrate the effect of various uncertainties regarding the 

input parameter distributional assumptions on the results presented in Section 2.4.1.

2.4.4 Uncertainties Associated with the Analysis

Uncertainties in the results presented in Exhibit 2-11 include the following sources:

• Uncertainties in the values used as input parameters.

• Assumptions inherent in the modeling equation (discussed in Section 2.2).

• Many aspects of uncertainty are not easily quantifiable. There are also many ways to look at 

uncertainty. An additional method we considered was a “bootstrapping” technique. The results 

from this technique are discussed in Appendix J.

Due to their importance in contributing to the total quantity of each chemical assumed to be 

disposed in a landfill, further information on the six most sensitive parameters identified in Exhibit 

2-13 is summarized below:

• Number of disposable wipes used per site per year in ‘Other Industries,’ and number of 

disposable wipes used per site per year in printing. The source of input data for these 

parameters is discussed in Section 2.3.1. While the number of data points available for 

describing these parameters is higher than for the other industries, high variability is expected 

for this parameter (e.g., due to differences in sizes of sites). Because of this, further evaluation 

of the effect of the input distribution assumption is conducted in Section 2.4.3.

• Number of generators per landfill. The source of input data for these parameters is discussed in 

Section 2.3.6. The input data depend on the estimated number of generators nationwide, the 

number of landfills nationwide, and regional (i.e., state to state) variations to account that 

generators and landfills are not distributed uniformly across the country. Evaluating variations 

at the state level allows for incorporation of regional variability; any further evaluations below 

the state level would require much more extensive data analysis as well as knowledge of the 

distribution and waste management practices of generators, which is unavailable or highly 

uncertain.

• Percent constituent in solvent. The source of input data for this parameter is discussed in 

Section 2.3.5. As with some of the other input data, high variability is expected for this 

parameter (e.g., due to differences in solvent formulations). Because of this, further evaluation 

of the effect of the input distribution assumption is conducted in Section 2.4.3.

• Solvent loading (grams/wipe) in ‘Other Industries’ and solvent loading in printing. The source 

of input data for these parameters is discussed in Section 2.3.2. The following are some of the 

uncertainties associated in developing these estimates:

o The quantity of solvent applied is variable, both in terms of variations at a single facility and 

variations from one facility to another.
30

o Notwithstanding the above, taken together most data (as collected from site visits and 

industry correspondence) reveals that the quantity of solvent applied spans one order of 

magnitude (10 to 100 grams) in most cases. Other input parameters (e.g., number of wipes 

used per site) have a higher range of variability. As a result, data limitations associated with 

this parameter are expected to have less impact on the variability of results than data 

limitations associated with other input parameters where the input parameter values are 

more variable.

o Further uncertainty results from assumptions used in estimating the quantity of solvent that

escapes from the wipe, such as a result of evaporation or liquid removal. In general, 

conservative assumptions were used to describe these effects.

Further assumptions are inherent in the modeling equation shown in Exhibit 2-1 and discussed in 

Section 2.2. Certain assumptions are made to simplify the analysis, or because of data limitations:

• It is likely that there is some degree of correlation or stratification of the various parameters. 

For example, facilities that use larger quantities of wipes may at the same time use smaller 

quantities of solvent per wipe (e.g., compare the data for the Electronics industry in Exhibit 2-1 

to other sectors). As data permitted, these effects were incorporated into the present analysis by 

making different assumptions for the different industry sectors. However, data limitations 

generally prevented making further assumptions.

• Insufficient data were available to assess if chemical concentration in solvent is likely to differ 

by industry. For example, the chemical concentration of one chemical (e.g., toluene) in a 

solvent used by the auto repair industry may be different from the chemical concentration in 

solvents used by the printing industry.

• The results of the analysis do not explicitly identify the probabilities that different chemicals are 

disposed in a single landfill. Exhibit 2-12 provides the results for each chemical; further 

assumptions are required in using these results to estimate the quantities of multiple chemicals 

at a landfill.

• Other than estimates made for the number of generators (Section 2.3.4), the input parameters do 

not account for future changes in generation or management practices. Most input parameters 

are based on ‘present day’ characteristics or data that was current at the time it was collected. 

Trends in the estimates are either unknown or difficult to quantify:

o As an example, it is not known if the quantity of wipes used by generators will increase or 

decrease in the future.

o The use of some specific chemicals can be reasonably expected to increase or decrease in 

the future. CalEPA (2004) investigated the use of alternative cleanup solvents in the 

printing industry. Acetone is exempt from VOC regulations and for that reason project 

sponsors tested its use as a substitute for existing solvents; as a result the use of acetone may 

increase in the future. Conversely, some chemical use data used in developing the input 

distributions were collected in preparation of NESHAP regulations (discussed in Appendix 

C); it is likely that use has since declined for some of these chemicals.
31

2.4.5 Numerical Stability for Disposable Wipe Analysis

Due to the nature of a probabilistic assessment, no two analyses using the same input data will yield 

identical results. This section assesses the numerical stability of the results presented in Exhibit 2-

11. Numerical stability refers to “the observed numerical changes in parameters of the output 

distribution (e.g., median, 95th percentile) from a Monte Carlo simulation as the number of 

iterations increases” (EPA, 2001).

To assess stability, duplicate analyses using identical input data (i.e., identical input parameter 

distributions) and identical numbers of iterations (identified in Section 1.1) were conducted for one 

constituent, methylene chloride. Results are shown in Exhibit 2-17. Exhibit 2-17 shows that the 

selected number of iterations gives stable results.

Exhibit 2-17. Results of Multiple Monte Carlo Analyses for Methylene Chloride in Disposable 

Wipes

Note

Result (kg/yr)

50th

Percentile

90th

Percentile

95th

Percentile

From Exhibit 2-6 465 3,268 5,583

Run 2 466 3,256 5,660

Run 3 467 3,232 5,549

Run 4 466 3,251 5,590

Run 5 466 3,240 5,608

Run 6 467 3,226 5,568

Average 466 3,246 5,593

Minimum 465 3,226 5,549

Maximum 467 3,268 5,660

Relative % Difference A 0.4% 1.3% 2.0%

A. Relative percent difference is calculated as the (maximum minus minimum)/average.

2.5 Solvent Extraction Technologies

Advanced solvent extraction technologies, such as centrifuging, will remove significant quantities 

of solvent from a towel or wipe. Various data are available regarding the performance of these 

technologies on towels and wipes. These data were not used in the landfill loading calculations, but 

are briefly discussed here and in Appendix A (in particular, Exhibit A-2) for potential use in further 

calculations. EPA (undated) includes data for a series of experiments conducted using an industrial 

centrifuge. Removal efficiencies are calculated for various towels and wipes and various solvents 

including isopropanol, methyl ethyl ketone, petroleum solvent, and press wash. Average extraction 

efficiencies range from 75 to 99 percent. Additional resources for evaluating solvent extraction 

technology performance are included in Appendix A.
32

3.0 Solvent Loadings Calculations: Laundries

The purpose of this section is to describe the methodology used to generate landfill loading 

estimates resulting from the disposal of industrial laundry sludge in a municipal solid waste landfill, 

and present those results. These landfill loading estimates serve as the basis for subsequent fate and 

transport modeling.

3.1 How Can Solvents Enter the Environment from Reusable Wipes?

Generators of shop and printer towels send these materials to industrial laundries. Some of these 

towels contain F001 to F005 solvents. When towels are laundered, their contaminants are removed 

and transferred to air, water, or solid media. When solid media (sludge) is disposed in a landfill, the 

contaminants in the sludge can be transferred to landfill leachate and be transported to a receptor.

Shop and printer towels, including solvent-contaminated towels, contribute contaminants to a 

laundry’s pretreatment system. To control these pollutants prior to a publicly owned treatment 

works (POTW) discharge, laundries conduct treatment practices such as oil separation, dissolved air 

flotation, or settling; alternatively, no treatment is conducted at all. The generated solids are 

managed as solid wastes using management methods such as landfilling or energy recovery.11

3.2 Overview of Calculation Methodology

Data characterizing quantities of contaminants present in laundry sludges from the washing of 

printer or shop towels are not available.12 As a result, the quantity of hazardous chemicals present 

in sludge was estimated from wastewater characterization and/or wipes loading assumptions. As 

with other analyses conducted in this report, only the compounds that are included in the definition 

of the F001 through F005 solvent listing were considered (e.g., other non-listed solvents and cocontaminants are not considered). Landfill loading estimates were developed for the same 20 

chemicals as considered in the disposable wipes analysis (see Section 1.2 for discussion of 

chemicals for which loading estimates are not made).

The purpose of the calculation is to estimate the quantity of chemical disposed in a single landfill 

per year. The overall calculation approach is presented below in Exhibit 3-1. This approach was 

used in estimating landfill loading quantities for 19 chemicals. Due to data availability, an alternate 

method (identified in Exhibit 3-2) is used for methanol. Exhibits 3-1 and 3-2 also identify the 

distributional assumptions used in the Monte Carlo analysis for each variable.

Exhibit 3-1. Sample Calculation for Estimating Landfill Loading of Acetone from Laundry 

Sludge

 11 Only the chemicals present in the laundry sludge are assessed in this report. There are other potential release points 

of chemicals from laundries which are not assessed in this report. These potential release points include volatilization to 

air at the laundry and the discharge of effluent.

12 As discussed in Section 3.3.3, limited TCLP data for the sludges are available.

Shop towel contribution Avg 

Value Distributional Assumption

A1 Shop towels washed at a site, lb/yr 312,893 Gamma distribution

C1 Shop towel water concentration, mg/L 3.98 Lognormal distribution; values depend on the 
33

Exhibit 3-2. Calculation for Estimating Landfill Loading of Methanol from Laundry Sludge

Parameter

Avg 

Value Distributional Assumption

G Methanol to laundry, lb/yr 40.5 Uniform distribution

E Sludge partitioning (fraction) 0.108 Step (constant)

F Number of Laundries/landfill 1.7 Logistic Distribution (minimum fixed at 1)

Unit conversion 0.454

 Result, kg chemical/yr in one landfill 3.38 (product of above four terms)

Exhibit 3-3 identifies the data used and its source in selecting parameter values for the variables 

referenced in the above equations. The results (mass chemical per landfill per year) from the 

equations are different for each chemical.

Exhibit 3-3. Source of Data for Laundry Sludge Calculations

Reference Variable Source How is Variability Assessed?

A1 & A2

Shop Towels 

and Printer 

Towels washed 

(pounds)

TRSA Survey (TRSA, 2010). 

Approximately 570 data points are 

available for shop towels and 

approximately 120 data points are 

available for printer towels (each data 

point represents a facility/ year).

The empirical data for the quantities of 

shop towels and printer towels 

processed were best-fit to gamma

distributions. These input towel 

processing quantities span several 

orders of magnitude.

B

% Laundries 

processing 

printer towels/ 

shop towels

TRSA, 2010. Value fixed at 21% (= 120/ 570) based 

on the TRSA data.

C1 & C2 Concentration 

in washwater

See detailed discussion below. 

Generally used Effluent Guidelines 

Development Data (ERG, 1999) with 

separate concentration data for shop 

towels and printer towels.

Assumed lognormal distributions 

when appropriate.

D

Gallons 

washwater 

generated per 

pound of towels

TRSA Survey (TRSA, 2010). The empirical data were best-fit to a 

lognormal distribution.

chemical (refer to Appendix I)

Printer towel contribution

A2 Printer towels washed at a site, lb/yr 196,605 Gamma distribution

C2 Printer towel water concentration, mg/L 49.7 Lognormal distribution; values depend on the 

chemical (refer to Appendix I)

B Probability that laundry manages printer 

towels in addition to shop towels 21% Step (Constant)

Other variables

D Gallon washwater/ lb towels 2.06 Lognormal distribution

E Sludge partitioning (fraction) 0.235 Step (Constant); values depend on the 

chemical

F Number of laundries/landfill 1.7 Logistic distribution (minimum fixed at 1)

Unit conversion 3.78E-06

Result, kg chemical/yr in one landfill 10.3 =[(A1×C1)+(A2×C2×B)]×D×E×F×[Unit 

conversion]
34

Reference Variable Source How is Variability Assessed?

E Sludge 

partitioning 

• Used results of partitioning 

equations developed from 2000 

WATER8 modeling simulation 

(ERG, 2000).

• Used frequency of DAF 

wastewater treatment (WWT) 

technology as estimated for the 

2006 Effluent Guidelines Plan 

(EPA, 2005)

Separate partitioning factors were 

developed for two different WWT 

systems. These were used in 

conjunction with the population 

estimate of each type of WWT 

technology.

F

Number of 

Laundries 

disposing 

sludge in a 

single landfill

Derived from Simmons, 2006 (for 

landfills by state) and Effluent 

Guidelines Development Data (for 

laundries by state).

Empirical averages were developed 

for each state and best-fit to a 

distribution.

G

Quantity of 

methanol sent 

to laundry 

(methanol only)

2004 TRI reporting for generators that 

identify sending methanol to an 

industrial laundry.

Narrow range was used based on 

available TRI data.

3.3 Data Used for Assessing Individual Variables

3.3.1 Quantity of Shop and Printer Towels Washed by Each Laundry

As part of its public comments submitted in response to EPA’s October 27, 2009 Notice of Data 

Availability, TRSA submitted survey data collected from its membership. The data represented the 

years 2005 to 2009, providing data from 11 companies, 271 laundries, and 569 location-years of 

data points. TRSA identified that the 271 laundries represent almost 90 percent of all facilities 

engaged in processing shop towels or printer towels. TRSA provided data regarding the quantity 

(pounds) of shop towels washed per year per facility, and the quantity (pounds) of printer towels 

washed per year per facility. Data used in this analysis are presented in Exhibit F-1, Appendix F. 

Graphical output comparing the assumed distributions to the source data are presented in Exhibits I3 and I-5 of Appendix I. TRSA’s data included approximately 570 data points for shop towels and 

approximately 120 data points for printer towels.

The TRSA data were fit to distributions. For each parameter, a gamma distribution was selected 

because it was either the best fit or the second best fit.

These data from TRSA were used preferentially to similar data from Effluent Guidelines 

development (ERG, 1999). The TRSA data has advantages and drawbacks in comparison to the 

Effluent Guidelines data. A primary advantage is that it is more recent (2005 to 2009) than the 

1990s Effluent Guidelines data. It also appears to include more facilities (271 as identified by the 

commenter), versus approximately 141 where data are available in the Effluent Guidelines data. A 

significant drawback to the TRSA data is that it appears to include data for multiple years for a 

single facility, with no method to distinguish which data are from a single facility. Therefore, it 

would provide bias towards those facilities providing multiple years of data over a facility providing 

a single year of data. Nevertheless, the advantages outweigh the disadvantages in this instance.

Additional data are available from the public comments to the OSW proposed rule for solvent wipes 

(RCRA-2003-0004); these include comments from several laundries identifying the quantity of 
35

towels processed by each. (A list of the laundry commenters with related information is found in 

Exhibit F-2, Appendix F.) However, the information/ data were not in a format compatible with the 

methods used in this analysis. The size of the data set is smaller than the TRSA data, and the public 

comments provided the number of towels rather than their weight. Pounds of towels processed are 

a more convenient measurement for the calculations (see Section 3.3.4 below, where washwater 

generation is expressed as gallons of water generated per pound of textile washed).

3.3.2 Fraction of Laundries Processing Printer Towels versus Shop Towels

As noted in Section 3.3.1, TRSA (2010) provided approximately 570 data points for shop towels 

and approximately 120 data points for printer towels. Based on these data it is reasonable to assume 

that more facilities process shop towels than process printer towels. As an approximation for this 

analysis, it is assumed that the fraction of facilities washing printer towels is 21 percent (120/ 570) 

of those washing shop towels. The TRSA data is not a true reflection of facility count, because a 

single facility can be represented by multiple data points (multiple years of data, as identified in 

Section 3.3.1). To provide consistency with the use of this data in Section 3.3.1, however, this 

estimate of 21 percent is used in the calculations.

The assumption made from the TRSA results is consistent with similar data from the Effluent 

Guidelines Technical Development Document (EPA, 2000), Table 4-3. This table identified the 

numbers (and percentages) of laundries which process shop towels and printer towels. This source 

showed that 1,332 laundries managed shop towels and 480 laundries manage printer towels (36%). 

Further, Effluent Guidelines data from ERG (1999) showed that the vast majority of laundries 

which process printer towels also process shop towels. 

For simplicity, the analysis assumes the following:

• The analysis ignores laundries that do not manage shop or printer towels.

• Of laundries managing shop towels and/or printer towels, it is assumed that all laundries manage 

shop towels.

• There is a 21 percent probability that the laundry also manages printer towels, using the 

distribution described in Exhibit 3-1.

• At the remaining laundries (i.e., 79 percent), there are no contributions from printer towels.

3.3.3 Concentration in Washwater

Exhibit 3-4 summarizes the sources of data used in assigning concentrations to constituents in 

laundry washwater. Primary reliance was placed on Office of Water sampling data. However, 

Office of Water data are not available for all 30 F-list constituents, and therefore additional data 

sources and assumptions are required.
36

Exhibit 3-4. Data Sources for Estimating Levels of Constituents in Laundry Washwater

Constituent Data Source for Concentration of Chemical in Shop 

Towel and Printer Towel Washwater

Acetone

Chlorobenzene

Cresols

Ethyl benzene

Methyl ethyl ketone

Methyl isobutyl ketone

Methylene chloride

Tetrachloroethylene

Toluene

Trichloroethylene

Xylenes (total)

Office of Water sampling results

1,2-Dichlorobenzene

1,1,2-Trichloroethane

Identified as not detected in any Office of Water sample (Effluent 

Guidelines Technical Background Document); assume equal to Office of 

Water detection limit

Benzene

Butanol 

Cyclohexanone

Ethyl acetate

2-Ethoxyethanol 

Isobutyl alcohol

No information in Effluent Guidelines Technical Background Document 

regarding analysis; assume equal to Office of Water detection limit

Methanol Estimated from TRI data; Section 3.3.7

Carbon disulfide 

Nitrobenzene

Pyridine

Assume zero (ND by Office of Water; no use assumed in Section 1.2)

Carbon tetrachloride 

Dichlorodifluoromethane

Ethyl ether

2-Nitropropane

1,1,1-Trichloroethane

1,1,2-Trichlorotrifluoroethane

Trichlorofluoromethane

Assume zero (no use assumed in Section 1.2)

Office of Water Data

The primary data source for identifying concentrations of chemicals in industrial laundry washwater 

is EPA’s Industrial Laundry effluent characterization study (EPA, 2000). EPA analyzed industrial 

laundry wastewater including wastewater from the washing of shop towels and printer towels. This 

washwater is representative of the washing of shop towels (or printer towels) when washed alone. 

In practice, laundries do not exclusively wash only shop towels and printer towels (see for example 

Appendix M of Comment No. RCRA-2003-0004-0348). By only accounting for the quantity of 

shop towels and printer towels processed, and not the entire throughput of the laundry (e.g., other 

materials such as mats and uniforms), this analysis accounts only for the quantity of chemical in the 

washwater and resulting sludge which originates from the shop and printer towels.

The Office of Water conducted sampling at numerous industrial laundries as part of effluent 

guidelines development for the 1997 proposed rule. The supporting record includes one sample of 

washwater generated from the washing of printer towels from each of three different industrial 

laundries (three samples total). Similarly, six samples of washwater generated from the washing of 

shop towels are available. Data for levels in shop towel washwater and printer towel washwater are 

available from Table DC-3 of the Office of Water Development Document (EPA, 2000). This is 
37

washwater directly from the washer with no pretreatment or dilution. The quantity in washwater 

may be less than the quantity in the towels received by the laundry due to evaporation or due to 

solvent recovery steps (if any) practiced by the laundry. 

Of the 30 F-list constituents, Office of Water data directly attributable to shop towel and printer 

towel washwater were available for 12 (i.e., the 11 identified in Exhibit 3-4 plus 1,1,1-

trichloroethane). Although 1,1,1-trichloroethane was detected in the Office of Water sampling, a 

concentration of zero is used for the calculation. For the reasons discussed in Section 1.2, it is 

assumed that use of this chemical in conjunction with shop towels or printer towels has fallen off 

dramatically between the time of the Office of Water sampling (in the mid-1990s) and the current 

day.

The Office of Water sampling results in EPA (2000) are presented as minimum, maximum, and 

average concentrations. These three values for each chemical were used for fitting the data to a 

lognormal distribution for all chemicals except methylene chloride and tetrachloroethylene in shop 

towel washwater. The lognormal distribution was assumed because this is a common distribution 

for environmental media13. The data are presented in Exhibit 3-5. Exhibits I-7 through I-28 in 

Appendix I present a graphical comparison of the data to the assumed distributions. For methylene 

chloride and tetrachloroethylene in shop towel washwater, a triangular distribution was used 

(corresponding to minimum, maximum, mean) because a lognormal distribution is not a reasonable 

representation of the input data at high concentrations (see Exhibits I-11 and I-13 in Appendix I; the 

mean of an assumed lognormal distribution is much higher than the maximum observed value).

Exhibit 3-5. Concentrations in Laundry Washwater

Chemical

Shop Towel Washwater 

Concentration, mg/L

Printer Towel Washwater 

Concentration, mg/L

Minimum Maximum Mean Minimum Maximum Mean

Chlorobenzene 0.01 1 0.252 0.1 1 0.467

Ethylbenzene 0.556 36 5.27 0.521 29.2 13.2

Methylene Chloride 0.01 39.9 4.22 0.14 1.54 0.614

Tetrachloroethylene 0.17 55.5 8.92 2.4 6.16 3.92

Toluene 1.11 11.6 5.33 14.1 33.2 20.5

Trichloroethylene 0.01 1 0.247 0.1 1 0.476

MEK 0.0898 15.8 5.4 2.05 5 3.09

Acetone 1 5.95 3.98 23.4 96.6 49.7

MIBK 0.132 5 1.88 0.5 5 2.07

Xylene 1.366 6.26 3.59 0.1 4.84 2.52

Cresol 0.02 1 0.305 0.1 1 0.433

Source: EPA, 2000.

Results for cresol in the effluent guidelines technical development document were shown as not 

detected for both shop towels and printer towels. This result was used as an estimate for a number 

of other constituents (as identified in Exhibit 3-4) which were either not detected or not analyzed 

during the Office of Water sampling. EPA (2000) identified that two chemicals (1,2-

dichlorobenzene and 1,1,2-trichloroethane) were not detected in any laundry wastewater (not just 

from shop towels and printer towels). For the remaining chemicals used as solvents, alternative 

data such as TRI were investigated as a basis for estimate; such data were available for methanol. 

For the remaining six constituents (benzene, n-butanol, cyclohexanone, ethyl acetate, 2-

ethoxyethanol, isobutyl alcohol), the concentration data for cresol (i.e., the Office of Water 

 13 RAGS (EPA, 2001), Appendix B, ‘Selection and Fitting of Distributions,’ Table B-2.
38

detection limit) were used. This value approximating the detection limit was selected because (1) 

no other data are available; (2) the detection limit is intended to represent a conservative but 

reasonable approximation of what levels could be present; and (3) based on usage data in Exhibit B1, Appendix B, the constituents are not widely present in solvents; therefore the concentrations in 

washwater would be expected to be less than concentrations of high-use solvents identified in 

Exhibit 3-4, such as methyl ethyl ketone and acetone.

Table DC-3 of EPA (2000) evaluates non-detect values as equal to the detection limit. For 

transparency, no attempt was made to modify the way that non-detects are used in the landfill 

loading calculations presented here. This assumption is conservative, because the actual 

concentration is less than the detection limit.

Ten chemicals were assumed to have zero concentration; these chemicals are discussed in Section 

1.2. Note that Exhibit 3-4 shows that three of these chemicals were analyzed and not detected in 

any of the laundry wastewater samples; one chemical (1,1,1-trichloroethane) was detected in the 

shop or printer towel analyses but is assumed not to be present in current-day washwater.

TCLP Data

UTSA/TRSA submitted results of TCLP analysis of laundry sludge to EPA, representing 22 

samples from 16 facilities analyzed for organics from the period 1993 to 2005 (UTSA, 2006). 

Detected constituents included the following:

• Chlorobenzene (detected in one sample)

• Cresol (detected in 4 samples)

• 1,2-Dichlorobenzene (detected in 2 samples)

• Ethyl benzene (detected in 1 sample)

• Tetrachloroethylene (detected in 6 samples)

• 1,1,1-Trichloroethane (detected in 1 sample)

• Xylenes (detected in 1 sample)

These data were not used quantitatively, but were used in conjunction with other information to 

identify the reasonableness that chemicals detected in the TCLP extract of the sludge would be 

expected to be present in the washwater. The data are presented in Exhibit F-3, Appendix F.

3.3.4 Quantity of Washwater Generated

Wastewater is generated from the washing of towels or other textiles. TRSA (2010) provided 147 

data points for the quantity of washwater generated per pound of textile. Similar advantages and 

limitations apply to the TRSA survey data for this parameter as was described in Section 3.3.1.

The calculation methodology (Exhibit 3-1) assigns these values to a distribution. A lognormal 

distribution was fit to the data because it was a second best fit and because TRSA suggests that a 

lognormal distribution would be reasonable. Data are presented in Exhibit F-1, Appendix F. 

Graphical outputs are presented in Exhibit I-33, Appendix I.
39

3.3.5 Sludge Partitioning

Once in the washwater, chemicals will be treated onsite if a laundry has a wastewater treatment 

system. The chemical in the washwater will partition to air or sludge, or remain in water. Because 

sludge samples were not collected, an air/water/solid partitioning model (WATER8) was used to 

estimate sludge loadings. The assumptions and results of this portioning model are provided in 

ERG (2000). The results are expressed as fraction of chemical in incoming washwater, which 

partitions to the sludge. Calculations were conducted with process assumptions that included the 

following:

• Reduction of volatility due to surfactant. The result is less chemical partitions to the air and 

more chemical partitions to water and sludge. The effect of reduced volatility due to surfactant 

(e.g., laundry cleaning detergent) was suggested during the effluent guidelines public comment 

period as an assumption yielding a more realistic scenario (ERG, 2000).

• Consideration of two separate wastewater management systems: (1) pit and screen; or (2) pit, 

screen, equalization, and dissolved air flotation (DAF unit). Each system results in different 

partitioning results. No other wastewater treatment processes were evaluated.

For these calculations, it is assumed that all laundries generate sludge/ solids for landfill 

disposal, with 55 percent using a pit and screen wastewater treatment method and 45 percent 

using a pit/ screen/ equalization/ DAF sequence. This may be conservative because some 

laundries do not pre-treat their wastewater (EPA, 2005) and presumably would not generate any 

sludge. The assumption that 45 percent use a pit/ screen/ equalization/ DAF sequence is based 

on information from the 2006 Effluent Guidelines Plan.14

Partitioning will vary depending on solvent-specific properties. The values used in the analysis are 

presented in Exhibit 3-6. Detailed calculations of this partitioning are presented in ERG (2000). 

Partitioning was also calculated using the quantity of solvent received by the laundry as the input 

quantity. This is different from the quantity of solvent in the washwater (discussed above) because 

some of the solvent will evaporate prior to entering the washwater. Specifically, ERG (2000) 

estimates that 36 percent evaporates prior to entering the washwater. This approach was only used 

for methanol where the quantity of chemical present in incoming laundry textiles was known.

Exhibit 3-6. Fraction of Chemical Partitioning to Sludge in Laundry Washwater

Chemical Scenario: WWT 

Settling pit + screen

Scenario: Settling pit + 

screen + EQ Basin + DAF

Weighted 

Average

Acetone 0.133 0.36 0.235

Benzene 0.212 0.504 0.343

Butanol 0.13 0.275 0.195

Chlorobenzene 0.115 0.184 0.146

Cresol 0.123 0.211 0.163

Cyclohexanone 0.43 0.646 0.527

1,2-Dichlorobenzene 0.126 0.234 0.175

 14 From EPA, 2005: “Based on the Uniform and Textile Service Association (UTSA) and the Textile Rental Service 

Association (TRSA) survey conducted in 1998, 55 percent of the industrial laundries do not pre-treat their wastewater. 

Of the remaining 45 percent of the facilities, the primary method of wastewater pre-treatment is dissolved air flotation 

(DAF).”
40

Chemical Scenario: WWT 

Settling pit + screen

Scenario: Settling pit + 

screen + EQ Basin + DAF

Weighted 

Average

Ethyl acetate 0.147 0.464 0.290

Ethyl benzene 0.368 0.606 0.475

2-Ethoxyethanol 0.209 0.504 0.342

Isobutyl alcohol 0.122 0.237 0.174

Methanol 0.119 0.219 0.164

Methyl ethyl ketone 0.141 0.419 0.266

Methyl isobutyl ketone 0.157 0.47 0.298

Methylene chloride 0.167 0.476 0.306

Tetrachloroethylene 0.259 0.536 0.384

Toluene 0.273 0.545 0.395

1,1,2-Trichloroethane 0.204 0.506 0.340

Trichloroethylene 0.222 0.512 0.353

Xylenes (total) 0.37 0.606 0.476

Source: ERG, 2000. Tables 3-7 to 3-12.

3.3.6 Number of Laundries Disposing Sludge in a Single Landfill

All laundry sludge is assumed to be disposed as a nonhazardous waste. Data from 2003 Biennial 

Reporting (Appendix D, in particular Exhibit D-6) and from public comments (UTSA, 2006) 

support that the vast majority of sludge is managed as nonhazardous.

Nationwide, there are approximately 1,600 MSW landfills. Approximately 1,300 laundries process 

shop towels and approximately 500 laundries process printer towels. Across the U.S., laundries and 

landfills are not distributed uniformly and therefore it is assumed that the number of laundries 

sending sludge to a single landfill can be greater than one (however, it will never be less than one). 

The following factors were assumed to affect this distribution:

• The number of laundries per state (using Office of Water data)

• The number of landfills per state (using Simmons, 2006 data)

• For each state, the percentage of MSW that is landfilled as opposed to being combusted. 

Laundry sludge disposal is assumed to follow this percentage.

The average number of laundries per landfill was calculated for each state using the above data 

(with a minimum of one laundry per landfill). This distribution (which ranged from 1 to 3) was 

used as part of the analysis. The values used in the analysis are presented in Exhibit F-4, Appendix 

F. Graphical outputs are presented in Exhibit I-34, Appendix I.

3.3.7 Estimates of Methanol Sent to Industrial Laundries

Methanol levels in washwater were not estimated using the techniques identified in Section 3.3.3. 

Instead, 2004 TRI data were used to determine that three generator sites report sending methanol to 

two different industrial laundries (it is assumed that the methanol is in the soiled textiles). These 

receipt quantities were used directly as the input quantity to the laundries. A fraction of this 

quantity was assumed to partition to the sludge, using the assumptions and data in Section 3.3.5. 

Exhibit 3-2 summarizes the data used in the methanol analysis.
41

3.4 Results

3.4.1 Loading Estimates

The above data were used as inputs for each of 20 chemicals (i.e., 10 chemicals identified in Section 

1.2 were not assessed). The calculations were conducted using Microsoft Excel®. Calculations 

were conducted using both deterministic (point-value) and Monte Carlo methods. The deterministic 

calculation was conducted by setting all inputs to their average values.

The Monte Carlo analysis was conducted with Excel® spreadsheets using Crystal Ball®. As 

discussed above, either a distribution or a fixed value is used for each input parameter (based on 

data availability). A separate calculation is conducted for each of the 20 chemicals. Certain input 

parameters are not affected by the evaluated chemical (e.g., pounds of shop towels processed), and 

are assessed using the same probability distribution for all chemicals. For each of 100,000 

iterations, all parameters are assessed independently (e.g., a facility accepting a large quantity of 

shop towels in one of the iterations may not necessarily also accept a large quantity of printer 

towels, or generate water with a high concentration of chemical). The output of a Monte Carlo 

analysis results in a distribution of values. A sample graphical Monte Carlo output is presented in 

Exhibit 3-7.

Exhibit 3-7. Monte Carlo Results for Acetone in Laundry Sludge

The values in Exhibit 3-8 represent landfill loading estimates for each chemical (i.e., the quantity of 

chemical that a single landfill will receive from large and small laundries). The Monte Carlo 

analysis provides percentile results from 0 to 100; for summary purposes the mean, median (50th

percentile), 90th percentile, and 95th percentile values are shown in Exhibit 3-8. The deterministic 

value, assessed by setting all input parameters to their mean values, is also presented (the sample 

calculation for this deterministic result was previously presented in Exhibit 3-1).

One use of the deterministic results is to provide a reference in reviewing the Monte Carlo results. 

For each chemical, the deterministic result is between the 50th and 90th percentiles of the Monte 

Carlo distribution, and less than the mean of the Monte Carlo results. For example, Exhibit 3-8 

identifies a deterministic result for acetone of 10.26 kg/landfill/year, a mean Monte Carlo result of 
42

12.34 kg/landfill/year, a 50th percentile Monte Carlo result of 2.53 kg/landfill/year, and a 90th

percentile Monte Carlo result of 26.40 kg/site/year. There are several reasons that the deterministic 

results are greater than the 50th percentile Monte Carlo results and less than the mean Monte Carlo 

result:

• In the probabilistic analysis, most laundries are assumed to accept only shop towels, with a 

small percentage (i.e., 21 percent) accepting both shop and printer towels. In the results for the 

probabilistic analysis, results towards the median are expected to be principally reflective of 

laundries washing only shop towels, while those towards the high end are expected to be 

reflective of laundries washing larger quantities of shop and printer towels. This assumption 

could not be precisely duplicated in the deterministic analysis, and results in the deterministic 

analysis results being greater than the 50th percentile probabilistic analysis.

• The shape of the input distributions additionally influences the results. This is due to the 

skewed shape of much of the input data (e.g., lognormal distributions); a characteristic of this 

assumption is that the average value is higher than the median value. This is shown in Exhibit 

3-9 for two input parameters. The deterministic analysis uses average values of the input 

parameters, and is another reason for the deterministic analysis results being greater than the 

50th percentile.

Exhibit 3-8. Landfill Loading Results for Laundry Sludge Evaluation

Constituent Result (kg/yr)

Deterministic 

(Mean Inputs)

Probabilistic

Mean 50th

Percentile

90th

Percentile

95th

Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane 

Ethyl ether

Nitrobenzene

2-Nitropropane

Pyridine

1,1,1-Trichloroethane

1,1,2-Trichlorotrifluoroethane

Trichlorofluoromethane

0 0 0 0 0

Acetone 10.26 12.34 2.53 26.40 53.02

Benzene 0.51 1.22 0.21 2.36 4.53

n-Butanol 0.29 0.67 0.13 1.36 2.61

Chlorobenzene 0.19 0.63 0.08 1.06 2.17

Cresols 0.24 0.58 0.11 1.14 2.16

Cyclohexanone 0.79 1.90 0.35 3.70 7.07

1,2-Dichlorobenzene 0.26 0.61 0.11 1.22 2.34

Ethyl acetate 0.43 1.03 0.17 1.96 3.89

Ethyl benzene 13.80 51.47 7.81 96.37 193.92

2-Ethoxyethanol 0.51 1.20 0.21 2.34 4.59

Isobutyl alcohol 0.26 0.63 0.11 1.21 2.35

Methanol 3.38 3.60 3.23 5.77 6.57

Methyl ethyl ketone 6.40 29.50 1.72 32.96 77.87

Methyl isobutyl ketone 2.66 5.12 1.00 10.52 19.54

Methylene chloride 5.45 20.74 9.25 51.02 78.46

Tetrachloroethylene 15.01 38.70 19.71 93.00 139.35

Toluene 13.15 17.30 6.10 43.05 69.22
43

Constituent Result (kg/yr)

Deterministic 

(Mean Inputs)

Probabilistic

Mean 50th

Percentile

90th

Percentile

95th

Percentile

1,1,2-Trichloroethane 0.51 1.23 0.21 2.33 4.53

Trichloroethylene 0.45 1.54 0.17 2.54 5.26

Xylenes (total) 7.73 9.57 4.65 21.98 33.26

Deterministic result is calculated using the mean (average) value of all input parameters.

For 10 chemicals, input concentration was set to zero; therefore, results are zero.

Exhibit 3-9. Comparison of Average and Median Values for Input Parameters 

Parameter Average Value Median Value

Shop towels washed at a site, lb/yr 312,893 226,116

Printer towels washed at a site, lb/yr 196,605 140,270

Number of laundries/landfill 1.7 2

Median input values are from the raw empirical data, and not from the parameters of the fitted distribution. 

See Appendix F for result.

3.4.2 Sensitivity Analysis

As with the analysis for disposable wipes, a sensitivity analysis was conducted for each of the input 

variables where a distribution or range of values was assigned. The approach used for estimating 

landfill loading from laundry sludge (shown in Exhibit 3-1) uses fewer input parameters than the 

approach for disposable wipes (Section 2), and therefore the sensitivity analysis procedure and 

results are slightly different.

Landfill loadings from laundry sludge result from shop towels and printer towels. The contributions 

from the washing of shop towels and the washing of printer towels are each significant, and are 

highly dependent on the chemical-specific concentration term. For example, contributions for 

acetone and MIBK are shown in Exhibit 3-10, using the deterministic equation from Exhibit 3-1.

Exhibit 3-10. Relative Contributions from the Washing of Shop Towels and Printer Towels

Contribution Acetone MIBK

kg/landfill/yr % kg/landfill/yr %

Shop Towels 3.9 38% 2.3 87%

Printer Towels 6.4 62% 0.3 13%

Total 10.3 100% 2.7 100%

Calculation is conducted using deterministic analysis.

To further evaluate sensitivity, rank correlation coefficients for each parameter were calculated 

using CrystalBall® software, using the same methods described in Section 2.4.2. Results of the 

sensitivity analyses in Exhibit 3-11 presents the parameters evaluated, the result for one chemical 

(methyl isobutyl ketone) arranged from most sensitive to least sensitive, and discussion of the 

results for the other chemicals evaluated. The comparisons for 19 chemicals (i.e., all except 

methanol, which was evaluated using different input parameters) are summarized in Exhibit 3-11.

From Exhibit 3-11, the two parameters associated with shop towels – quantity of shop towels 

washed and shop towel water concentration – are the two most sensitive parameters for the majority 
44

of chemicals evaluated and can be considered the most important with respect to their influence on 

results.

Exhibit 3-11. Sensitivity Analysis for Laundry Sludge Analysis

Parameter Result for MIBK Typical Order of Sensitivity for 19 

Chemicals A

Shop towel water concentration, 

mg/L 58% This parameter was the most sensitive for 14 of 19 chemicals, and second-most sensitive for four others.

Shop towels washed, lb/yr 25%

This parameter was the second-most sensitive for 14

of 19 chemicals, and most sensitive for the remaining 

five chemicals.

Sludge partitioning (fraction) 6.7% This parameter was neither the most nor the least 

sensitive.

Probability that laundry 

manages printer towels in 

conjunction with shop towels

3.7% This parameter was the third-most sensitive for 10 of 

19 chemicals.

Gallons washwater/lb towels 3.4% These parameters were neither the most nor the least 

Number of laundries/landfill 2.3% sensitive.

Printer towels washed, lb/yr 0.6% These parameters were the least sensitive for all of 

19 chemicals. Printer towel water 

concentration, mg/L 0.3%

The sensitivity results for each parameter are in units of ‘percent’ and total to 100%.

A. Sensitivity analyses for methanol are not presented in this exhibit. As described in Section 3.3, methanol was 

assessed using a different calculation than the other 19 chemicals.

3.4.3 Effects of Input Parameter Distribution Assumptions on Results

The results presented in Section 3.4.1 are based, in part, on assumptions made with regard to the 

distribution of each input parameter. To assess the significance of changes in the input parameter 

distribution assumptions on the results, several ‘what-if’ analyses are shown in this section.

As described in Section 1.3, most of the assumptions were selected using a ‘goodness of fit’ test: the 

assumption with the best fit was selected. The purpose of this section is to describe the impact on 

results when changing these assumptions. The following conditions are used in this section as a 

basis for selecting a different distribution assumption for an input parameter:

• When two or more distributions provide a ‘good fit’ to the data. As discussed in Section 1.3, 

generally the assumption providing the best fit is used in the analysis because there is typically 

no information to assess which distribution assumption would be most appropriate for the 

population from a purely mechanistic perspective. This section examines the impact that an 

alternative assumption would have on the results. Appendix I summarizes the results for four 

input parameters and includes graphs comparing the selected distributions to the empirical data.

• When data outliers may exist. Potential outliers in the data inputs are identified in this section 

and an alternative distribution, without the outlier, is developed. When an outlier exists it does 

not mean it should necessarily be removed, because it may be representative of a tail of a 

population. Instead, the purpose of this analysis is to evaluate the effects that such an 

assumption may have on the results. This evaluation is conducted, in part, to investigate 

impacts of uncertainties in the underlying data. Appendix I summarizes the results for two input 

parameters.
45

• In this section, analyses are limited to those input parameters where sufficient data exists to 

evaluate alternate assumptions:

o Quantity of shop towels washed per laundry per year.

o Quantity of printer towels washed per laundry per year.

o Chemical concentration in shop towels.

o Chemical concentration in printer towels.

To assess the significance of changes in the input parameter distribution assumptions on the results, 

‘what-if’ analyses are presented in this section. In these analyses, the assumption regarding the 

shape of the input distributions is changed, one at a time, for several input parameters. This 

analysis does not include all possible combinations of the different assumptions. The procedure and 

results are shown in Exhibits 3-12 and 3-13 for two chemicals, acetone and toluene, respectively.

For the concentration terms for the laundry sludge analysis, the input parameter is assumed to be 

described by a lognormal distribution (lognormal distributions of environmental concentrations are 

a common assumption in other analyses).15 An evaluation was conducted to investigate the impact 

of this assumption, by assuming that triangular distributions describe the distribution due to the 

availability of data described in Section 3.3.3. Exhibits 3-12 and 3-13 show how results are 

impacted by this assumption change.

Appendix I includes graphs comparing the selected distributions to the empirical data for each of 

the above parameters.

 15 RAGS Volume 3 Part A, Appendix B, ‘Selection and Fitting of Distributions,’ Table B-2.
46

Exhibit 3-12. Effect of Changes in Input Parameter Distributions on Results for Acetone

Parameter

How was 

Distribution 

Changed?

Reason for Change

Result, 

50th

Percentile

Result, 

90th

Percentile

Result, 95th

Percentile

“Baseline” No change 2.53 26.40 53.02

Scenario 1: 

Quantity of shop 

towels washed per 

laundry, lb/year

Gamma to 

Lognormal

Per TRSA (2010), 

lognormal was expected to 

provide a good fit for the 

data.

2.38 28.68 56.38

Scenario 2: 

Quantity of printer 

towels washed per 

laundry, lb/year

Gamma to 

Lognormal

Per TRSA (2010), 

lognormal was expected to 

provide a good fit for the 

data.

2.54 25.44 54.47

Scenario 3: 

Quantity of shop 

towels washed per 

laundry, lb/year

Eliminate 

potential 

outlier; reevaluate with 

gamma

Per Table I-2, the maximum 

value is a potential outlier. 

A gamma distribution is the 

best fit for the new data set.

2.51 26.09 52.07

Scenario 4: 

Quantity of printer 

towels washed per 

laundry, lb/year

Eliminate 

potential 

outlier; reevaluate with 

beta

Per Table I-2, the maximum 

value is a potential outlier. 

A beta distribution is a good 

fit for the new data set.

2.53 27.62 52.67

Scenario 5: 

Washwater 

concentration for 

shop towels, mg/L

Lognormal to 

triangular

Assess effect of a 

simplifying assumption. 2.94 24.11 49.99

Scenario 6: 

Washwater 

concentration for 

printer towels, 

mg/L

Lognormal to 

triangular

Assess effect of a 

simplifying assumption. 2.56 28.15 55.35

Results for ‘baseline’ case are identical to those in Exhibit 3-8.
47

Exhibit 3-13. Effect of Changes in Input Parameter Distributions on Results for Toluene

Parameter

How was 

Distribution 

Changed?

Reason for Change

Result, 

50th

Percentile

Result, 

90th

Percentile

Result, 95th

Percentile

“Baseline” No change 6.10 43.05 69.22

Scenario 1: 

Quantity of shop 

towels washed per 

laundry, lb/year

Gamma to 

lognormal

Per TRSA (2010), 

lognormal was expected to 

provide a good fit for the 

data.

5.74 45.70 76.94

Scenario 2: 

Quantity of printer 

towels washed per 

laundry, lb/year

Gamma to 

lognormal

Per TRSA (2010), 

lognormal was expected to 

provide a good fit for the 

data.

6.02 43.50 74.19

Scenario 3: 

Quantity of shop 

towels washed per 

laundry, lb/year

Eliminate 

potential 

outlier; reevaluate with 

gamma

Per Table I-2, the maximum 

value is a potential outlier. 

A gamma distribution is the 

best fit for the new data set.

6.10 42.81 69.77

Scenario 4: 

Quantity of printer 

towels washed per 

laundry, lb/year

Eliminate 

potential 

outlier; reevaluate with 

beta

Per Table I-2, the maximum 

value is a potential outlier. 

A beta distribution is a good 

fit for the new data set.

6.16 42.64 67.07

Scenario 5: 

Washwater 

concentration for 

shop towels, mg/L

Lognormal to 

triangular

Assess effect of a 

simplifying assumption 7.93 38.21 58.37

Scenario 6: 

Washwater 

concentration for 

printer towels, 

mg/L

Lognormal to 

triangular

Assess effect of a 

simplifying assumption 6.09 43.22 69.41

Results for ‘baseline’ case are identical to those in Exhibit 3-8.

Most of the results in Exhibits 3-12 and 3-13 are similar, indicating that changes in input parameter 

assumptions have minor effects on results.

The results presented in this section demonstrate the effect of various uncertainties regarding the 

input parameter distributional assumptions on the results presented in Section 3.4.1.

3.4.4 Uncertainties Associated with the Analysis

Uncertainties in the results presented in Exhibit 3-8 include the following:

• Data for one of the most sensitive parameters, quantity of shop towels washed, are based on a 

large sample size from a recent TRSA survey. As a result, a high degree of confidence in this

parameter is expected. Nevertheless, the data include some bias in that some facilities are 

represented in the data set for multiple years. In addition, it is not known if the outliers 

identified in Section 3.4.3 are representative of the population.

• Data for another sensitive parameter, chemical concentration data for shop towels, are available 

for a limited number of samples (six or less), and the source material provides only the summary 
48

statistics (i.e., minimum, maximum, mean). Due to the combination of these factors, the results 

in Exhibit 3-8 may not reflect the full range of variability associated with this input parameter.

• Number of laundries per landfill. As with the disposable wipes analysis, data were evaluated at 

the state level to evaluate regional variability. Any further evaluations below the state level 

would require much more extensive data analysis as well as knowledge of the distribution and 

waste management practices of laundries that is unavailable or highly uncertain.

• The results of the analysis do not explicitly identify the probabilities that different chemicals are 

disposed in a single landfill. Exhibit 3-8 provides the results for each chemical; further 

assumptions are required in using these results to estimate the quantities of multiple chemicals 

at a landfill.

• As with the disposable wipes analysis, the input parameters do not account for future changes in 

generation or management practices. Most input parameters are based on ‘present day’ 

characteristics or data that was current at the time it was collected. Similar uncertainties 

discussed in Section 2.4.4 are applicable here.

3.4.5 Numerical Stability for Laundry Sludge Analysis

Due to the nature of a probabilistic assessment, no two analyses using the same input data will yield 

identical results. This section assesses the numerical stability of the results presented in Exhibit 3-8. 

Numerical stability refers to “the observed numerical changes in parameters of the output 

distribution (e.g., median, 95th percentile) from a Monte Carlo simulation as the number of 

iterations increases” (EPA, 2001).

To assess stability, duplicate analyses using identical input data (i.e., identical input parameter 

distributions) and identical numbers of iterations (identified in Section 1.1) were conducted for one 

constituent, tetrachloroethylene. Results are shown in Exhibit 3-14. Exhibit 3-14 shows that the 

selected number of iterations gives stable results particularly at the upper percentiles of most 

concern.

Exhibit 3-14. Results of Multiple Monte Carlo Analyses for Tetrachloroethylene in Laundry 

Sludge

Note

Result (kg/yr)

50th

Percentile

90th

Percentile

95th

Percentile

From Exhibit 3-8 19.71 93.00 139.35

Run 2 19.85 93.15 137.76

Run 3 19.78 92.55 138.19

Run 4 19.73 93.35 137.67

Run 5 19.67 93.55 139.98

Run 6 19.80 92.65 138.44

Average 19.76 93.04 138.57

Minimum 19.67 92.55 137.67

Maximum 19.85 93.55 139.98

Relative % Difference A 0.9% 1.1% 1.7%

A. Relative percent difference is calculated as the (maximum minus minimum)/average.
49

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Hazardous Solvents. Document No. RCRA-2003-0004-0045. February 2000.

ERG, 1999. Annual Sludge Generation Amounts for the 190 In-scope Industrial Laundries. 

Document No. RCRA-2003-0004-0043. September 1999.
52

Kimberly-Clark, 1997. Letter from Ralph Solarski, Kimberly-Clark Corporation, to Jim O’Leary, 

U.S. EPA, regarding information for disposable wipes. Document No. RCRA-2003-0004-0109. 

July 25, 1997.

Maratek, 1998; Document No. RCRA-2003-0004-0112.

Montreal, 2000. http://hq.unep.org/ozone/Montreal-Protocol/Montreal-Protocol2000.shtml

PNEAC, 1996. Keller Crescent Company’s Solvent Recovery Solution. Circa 1996. 

http://www.pneac.org/sheets/litho/Kellercrescent.pdf

Printstep, 1999. PrintStep Printers' Plain Language Workbook Template (EPA), Appendix C. 

Circa 1999.

RCRA-2003-0004 Docket. Hazardous Waste Management System: Identification and Listing of 

Hazardous Waste: Conditional Exclusions From Hazardous Waste and Solid Waste for SolventContaminated Wipes, Final Rule.

Salman, 2006. Communication with Dave Salman (EPA), June, 2006 email.

SGIA 1996. Memorandum from Marcia Kinter (Screenprinting and Graphic Imaging Association 

International) to Jim O’Leary (EPA). Document No. RCRA-2003-0004-0107. February 20, 1996.

Simmons, 2006. Simmons, Phil; Goldstein, Nora; Kaufman, Scott; Themelis, Nickolas; Thompson, 

James. The State of Garbage in America. BioCycle. Volume 47, No. 4, pages 26-43. April 2006. 

http://www.jgpress.com/archives/2006_04.html

Spectrum, 2001. Chemical Information from Spectrum Laboratories. 

http://www.speclab.com/compound/

TRSA, 2010. Letter from TRSA to EPA Re: Docket ID No. EPA-HQ-RCRA-2003-0004. February 

26, 2010. Document No. RCRA-2003-0004-0461.

UTSA, 2006. Letter from UTSA/TRSA to EPA Re: EPA's Revised Risk Assessment for the 2003 

Shop Towel Proposal. September 12, 2006. Document No. RCRA-2003-0004-0396. 
53

Appendix A. Supporting Data
54

Data in Exhibit A-1 were used, in part, to develop input parameters including towel and wipe usage rates for the disposable towel and 

wipe analysis. The final column identifies if the data were used in the evaluation; a (---) indicates the data were not used, typically 

because use rates were not available. Appendix B presents the actual values used in the analysis.

Exhibit A-1. Shop Towel and Wipe Usage Rates

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Docket Item No. 

0052 --- ‘Small sites’ 1995 2,000 to 3,000 lb/year Disposed Other manufacturing

Docket Item No. 

0099 ---

4 automobile 

manufacturing 

sites

1994

--- Laundered Petroleum naphtha ---

--- Disposed Petroleum naphtha/ xylene mix ---

--- Laundered 50% Petroleum naphtha/ 50% 

IPA mix ---

--- --- 26% acetone/ 9% toluene/ 

65% petroleum naphtha mix ---

Docket Item No. 

0102

Member 

survey

Flexible 

packaging 

manufacturing 

facilities

2001 100-1,000/day Disposable and 

Laundered

Acetone

Butyl alcohol

Ethyl acetate

MEK

MIBK

Toluene

Also, petroleum napthas which 

may contain low levels of 

BTEX, etc.

Other manufacturing

Docket Item No. 

0104 --- Kimberly-Clark Corporation 1995

Wiper sales 2,500-

50,000/ month. But 

only 30% of facilities 

used wipers with 

solvents

Disposable

Acetone

Methyl ethyl ketone

Toluene

Xylene

---

Docket Item No. 

0105 --- Printing 1995

“Common” printing solvents 

include:

XCELLO contains 15% MEK; 

2-ethoxy ethanol 10%; 

Toluene 65%

REPROKLENE 104 contains 

Xylene 30%; methylene 

chloride 20%; ethyl benzene 

6%

---

Docket Item No. 

0106

63 

respondents

Printing (Flexible 

packaging & 

others)

1996 10-100+/day

87% use 

Laundered; 

~30% use 

disposable

# of sites using solvents:

Acetone: 5%

Toluene: 6%

MEK: 5%

Methylene chloride: 3%

Trichloroethane: 3%

Acetates: 19%

Printing
55

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Docket Item No. 

0107

13% of 

membership 

(5200 of 40000 

sites)

Screen & 

Graphic Imaging 

Association 

International: 

50% textile 

screen printing; 

50% graphics

1996

1-25/day: 17%

25-50/day: 17%

50-100/day: 17%

Disposable and 

Laundered

# of sites using solvents (Not 

exhaustive, but represents 

principal products):

MEK: 18%

Acetone: 27%

Xylene: 19.5%

Toluene: 20%

Also, Lacquer thinner or 

mineral spirits which may 

contain low levels of BTEX, 

etc.: 25%

Printing

Docket Item No. 

0113

Member 

survey

SGIA: Screen 

printing sites 1998

1,108 reusables/ 

week; 3,164 

disposables/ week

60% of screen 

printing & 

flexographic use 

laundered; 40% 

use disposables

--- Printing

Docket Item No. 

0113

Member 

survey

SGIA: 

Flexographic 

sites

1998

2,145 reusables/ 

week; 1,217 

disposables/ week

--- Printing

Docket Item No. 

0113

Member 

survey

Screen & 

Graphic Imaging 

Association 

International -

Lithographic

1998 827 reusables/ week

99% of 

lithographic 

printers use 

laundered

--- Printing

Docket Item No. 

0115 1 site Printer 1995 12,000/week Disposables --- Printing

Docket Item No. 

0116 1 site Auto 

manufacturer 1999

Concentration in the solvent 

blend raw material :

Xylene 30-50%;

Methanol <2%

Ethyl benzene <6%

Also petroleum distillates

---

Docket Item No. 

0118

Member 

survey (7 sites)

Semiconductor 

manufacturing 

Industry

1999 1,500/day Primarily 

Disposed

Primarily isopropanol (not an 

F-list solvent)

Less frequently: acetone, 

methanol

< 10 ml/application

Electronics

Docket Item No. 

0119

Member 

survey

Aerospace 

Industries 

Association

1999 Range from 25/ day 

to >1000/ day

Primarily 

Disposal (60-

100%)

MEK, TCE, acetone, ethyl 

acetate, MIBK, toluene, 

xylene, methanol, 

cyclohexanone, 

perchloroethylene, 

Also mineral spirits, naptha, 

paint thinner, stoddard solvent.

Use of non-hazardous solvents 

range from 0-80%

50-250 ml/application

Transportation
56

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Docket Item No. 

0120

1 site 

(medium-sized 

printer)

Printing 1999 420,000/year Reusable --- Printing

Docket Item No. 

0122 5 sites

Various sites: 

Paint/ ink 

manufacturing; 

auto 

manufacturing; 

general 

machining

2000

16 to 32 drums/ 

month (Docket Item 

0120 uses 800 

towels/ drum; using 

this conversion gives 

13,000 to 25,000/ 

month)

Solvent 

extraction (not 

identified as 

laundered/ 

disposable)

Typical: toluene, xylene, MEK, 

butyl alcohol, acetone, 

methanol

General 

manufacturing

Docket Item No. 

0123 1 site? 5 sites? Printing 1999 Used 109,000 pounds solvent/year Disposables --- ---

Cal/EPA, 2004 10 California 

printers

Cross section of 

lithographic 

printing

2004 --- Reusables

All use solvents for cleanup; 

some have F-listed 

constituents per MSDSs:

Acetone (6%)

Benzene (<10 ppm)

Glycol ether (10-12%) 1

Xylene (1.5%-3.5%)

Mineral spirits or other HCs 

(with trace quantities of 

benzene, toluene, xylene)

Project tested acetone as 

potential replacement, to meet 

purpose of lowering VOC

---

Beyer, 2003 - Average from 

137 auto shops 1996 641 shop towels per month Laundered --- Auto Repair

Beyer, 2003 -

Average from 53 

manufacturing 

sites

1996 Less than auto shop 

use

Laundered --- Other Manufacturing

Beyer, 2003 M&M Precision 

– Dayton, OH

Precision 

Measuring 

Equipment

c. 2003 200/ week Laundered --- Other Manufacturing

Beyer, 2003

CSS 

International 

Corporation –

Philadelphia, 

PA

MFR Machine 

Shop c. 2003 300/ week Laundered --- Other Manufacturing

Beyer, 2003 Avibank – N. 

Hollywood, CA Aerospace c. 2003 2000/ week Laundered --- Transportation

Beyer, 2003

Tie Down 

Engineering –

Atlanta, GA

Steel Fabrication c. 2003 500/ week Laundered --- Other Manufacturing
57

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Beyer, 2003

Select Tool & 

Die 

Corporation –

Dayton, OH

Manufacturing c. 2003 5,000/ year Laundered --- Other Manufacturing

Beyer, 2003

Ames Safety 

Envelope –

Somerville, MA

Printing c. 2003 400,000/ year Laundered --- Printing

Beyer, 2003

Integrated 

Aerospace –

Santa Ana, CA

Aerospace c. 2003 350/ week Laundered --- Transportation

Beyer, 2003

North Star 

Steel 

Company –

Wilton, IA

Steel Fabrication c. 2003 600/ week Laundered --- Other Manufacturing

Beyer, 2003

Johnson 

Controls –

West Union, 

SC

Injection molding c. 2003 250/ week Laundered --- Other Manufacturing

Beyer, 2003

Hydroform 

USA – Carson, 

CA

Aerospace c. 2003 1,000/ week Laundered --- Transportation

Beyer, 2003 PAK 2000 –

Lancaster, PA

Specialty 

packaging c. 2003 75,000/ year Laundered --- Other Manufacturing

Beyer, 2003

Porter 

Engineered 

Systems –

Westfield, IN

Automotive c. 2003 375/ week Laundered --- Transportation

Beyer, 2003

AW North 

Carolina Inc. –

Durham, NC

Automotive 

bearings c. 2003 1,000/ week Laundered --- Other Manufacturing

Beyer, 2003

Intermet –

Minneapolis, 

MN

Metal fabrication c. 2003 4,000 to 5,000/ week Laundered --- Other Manufacturing

Beyer, 2003

Minnesota 

National Guard 

– St. Paul, MN

Aircraft c. 2003 500 to 1,000/ week Laundered --- Transportation

Beyer, 2003

Briggs & 

Stratton –

Auburn, AL

Small engine 

manufacturing c. 2003 1,000/ week Laundered --- Other Manufacturing

Beyer, 2003

Boral Bricks –

Phoenix City, 

AL

Brick 

manufacturing c. 2003 1,000/ week Laundered --- Other Manufacturing

Beyer, 2003

Marvin 

Engineering –

Inglewood, CA

Manufacturing c. 2003 200/ week Laundered --- Other Manufacturing
58

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Beyer, 2003

HXM Motor 

Express –

Harrisonburg, 

VA

Transportation c. 2003 500/ week Laundered --- Transportation

Beyer, 2003 Parker –

Manitowoc, WI Hydraulics c. 2003 8,000/ week Laundered --- Other Manufacturing

Docket Item No. 

0130

John Roberts 

Company –

Minneapolis, 

MN

Commercial 

Printer 1991 7,000 wipers per 

week Laundered

Solvent: acetone, toluene, 

MEK

Solvent: Toluene, 24% 1,1,1-

trichloroethane (to be 

discontinued by 1993)

Printing

Docket Item No. 

0280

Los Álamos 

National 

Laboratory –

Los Alamos, 

NM

R&D c. 2003 252 kilograms per 

year Disposable --- ---

Docket Item No. 

0292 - colleges and universities -

0 to 40/ daily 

(Average of <15 

wipes/ daily)

- --- ---

Docket Item No. 

0387

Deerfield –

Ohio

Large 

manufacturing 1999

376/ week Laundered --- ---

310/ week Disposable --- Other Manufacturing

Docket Item No. 

0387

Aeroblaze –

Ohio

Large 

manufacturing 1999

297/ week Laundered --- ---

227/ week Disposable --- Other Manufacturing

EPA, 2006b

Corporate 

Press –

Landover, MD

Commercial 

printer 2006

1,500 /week (down 

from historical 

4,000/week)

Laundered --- Printing

EPA, 2006c

Exceptional 

Engraving 

Company –

Minneapolis, 

MN

Printing/ 

engraving 2006 400 to 500/ month Laundered --- Printing

PNEAC, 1996

Keller 

Crescent 

Company –

Evansville, IN

Commercial 

printer c. 2000 210,000/ year Laundered --- Printing

EPA, 2006e

McArdle 

Printing 

Company, Inc. 

– Upper 

Marlboro, MD

Commercial 

printer 2006 2,200 to 3,000/ week Laundered --- Printing

EPA, 2006f

NilfiskAdvance –

Plymouth, MN

Equipment 

assembly: All 

operations

2006

9,000/ month (down 

from historical 

30,000/week)

Laundered --- Other Manufacturing
59

Reference A Site/Location Business Type Year Total Usage Management F-Listed Solvents Used Apply Wipe Usage 

to Sub-sector

Plastics only 2006 200-300/week Laundered methyl ethyl ketone Other Manufacturing

Printing only 2006 125/month Laundered --- Printing

EPA, 2006g

Royal 

Business 

Forms & 

Printing –

Brooklyn Park, 

MN

Commercial 

printer 2006 2,500 to 4,000/ week Laundered --- Printing

EPA, 2006h

SpecialMade 

Goods & 

Services –

Winchester, 

VA

Printing 2006

1,000/ year (down 

from historical 2,000-

3,000/year)

Disposable Glycol ethers16 Printing

EPA, 2006i

Wipaire – Inver 

Grove Heights, 

MN

Manufactures 

floats for 

seaplanes

2006 3,000lb/ year --- Methyl ethyl ketone Other Manufacturing

EPA, 1997 Site visit Printing 1997

2,400 wipes/month 

reusable; 0 

disposable

Laundered Printing

EPA, 1997 Site visit Printing 1997

320 wipes/month 

reusable; 1200 

disposable

Laundered & 

disposed Printing

EPA, 1997 Site visit Auto body 1997

250 wipes/month 

reusable; 2000 

disposable

Laundered & 

disposed Auto repair

EPA, 1997 Site visit Auto 

maintenance 1997

100 lbs/month 

reusable; 50 

lbs/month disposable

Laundered & 

disposed Auto repair

EPA, 1997 Site visit Aircraft 

maintenance 1997

Reusable not avail; 

1,200 disposable 

wipes/ month

Laundered & 

disposed Transportation

EPA, 1997 Site visit Circuit Board 1997 0 Reusable; 2,000 

disposable Disposed Electronics

EPA, 1997 Site visit Aerospace 

instrument mfg 1997

480 wipes/month 

reusable; 40 

disposable wipes/ 

month

Laundered & 

disposed Other Manufacturing

A. All numbered docket items are referenced from the RCRA-2003-0004 Docket.

 16 Precise glycol ether not specified. Although 2-ethoxyethanol is a glycol ether, it would likely be uniquely listed on the MSDS due to TRI reporting requirements.
60

Data in Exhibit A-2 were used, in part, to develop input parameters including solvent application rates for the disposable towel and wipe 

analysis. The final column (labeled “*C*”) identifies if the data were used in the evaluation; a “*” indicates the data were used. A blank 

in the final column indicates the data were not used, typically because data were not available to estimate a solvent quantity per wipe. 

Appendix B presents the actual values used in the analysis.

Exhibit A-2. Solvent Application to Shop Towels and Wipes

Reference Type of Towel/ 

Wipe & Industry

Activity Weight of Dry 

Towel/ Wipe

Weight of Wet 

Towel/ Wipe

Notes *C*

Docket Item No. 0110 Customer base 10 grams solvent per wipe *

Docket Item No. 0118 Disposable/ Semiconductor 

Manufacturing Use quantity <10 mL solvent per wipe *

Docket Item No. 0127 Laundered shop towel Hand wringing until 

no drips 29 grams 85 grams Avg of 34 samples, using acetone *

EPA, 2006b Laundered / Printing Use quantity 49 grams 135 grams

Also sampled used rags 

from screen bottom drum. 

Top: 161 g & 94 g. 

Bottom: 140 g. Variation 

is due, in part, on amount 

of ink on towel.

*

EPA, 2006c Laundered / Printing Use quantity

7-18 grams solvent 

added/ wipe (calculated 

from use data)

Uses 1-2 quarts pet 

naphtha/ week; 400-500 

wipes/month

*

PNEAC, 1996 Laundered / Printing Use quantity 28 grams solvent removed 

via recovery process

Process recovers 5 gallon 

solvent/ 600 towels *

EPA, 2006d Laundered / Manufacturing Disposed Quantity -

30 gallons/ week of 

solvent recovered via 

centrifuge

EPA, 2006e Laundered wipe / Printing Disposed Quantity 64 grams

107 grams (95-122 

grams) Variation is due, in 

part, on amount of ink on 

towel.

Average weight of 8 

towels throughout screenbottom drum*

EPA, 2006f Laundered/ Assembly Use quantity 12-18 grams/ towel Solvent: MEK *

EPA, 2006i -/ Floats for seaplanes - 76 grams - -

EPA, 2006h Disposable/ Printing Use quantity - 102 grams/ towel 

(calculated)

Not all solvent may be 

applied to rag; calculated 

use may be 

overestimated

EPA, 2006g Laundered/ Printing Disposed Quantity - 34-426 grams/ towel -

Docket Item No. 0062 (1997) 

- UTSA

Laundered shop towels Solvent Disposed 28.4 g 62.5 to 135 lb VOC/ 1000 

lb towels

Disposable paper towels --- 6.7 g ---

Docket Item No. 0090 (2001) 

- Joe Mattson @ Industrial 

Towel and Uniform (ITU)

Laundered shop towels

ITU’s New Berlin 

plant recovers 10,000 

gallons of solvent per 

month

--- ---
61

Reference Type of Towel/ 

Wipe & Industry

Activity Weight of Dry 

Towel/ Wipe

Weight of Wet 

Towel/ Wipe

Notes *C*

Docket Item No. 0099 (1994) 

- American Automobile 

Manufacturers Association

Auto manufacturing single 

site/ Laundered Solvent Disposed 42.6 g 51,434 lb/yr in spent towel/ wipe

Solvent used> solvent 

disposed, indicating 

evaporation during use

Auto manufacturing single 

site/ Disposable Solvent Disposed 15.5 g 4,544 lb/yr in spent towel/ wipe

Auto manufacturing single 

site/ Laundered Solvent Disposed 31.2 g 1,448 lb/yr in spent towel/ wipe

Auto manufacturing single 

site/ (reusable or disposable 

not specified)

Solvent Disposed 30.5 g 5,547 lb/yr in spent towel/ 

wipe

Docket Item No. 0108 (No 

date) - Jeffrey Adrian @ The 

John Roberts Company

Shop towels (reusable or 

disposable not specified)

Centrifuging for 

solvent recovery 1.3 oz 3 oz (before centrifuge) & 1.64 oz (after centrifuge) At printing site *

Docket Item No. 0115 (1995) 

- Paul Wavrock @ 

MARATEK Environmental 

Technolologies, Inc.

Disposable & reusable shop 

towels

Solvent quantity in 

used towels --- 1 gal solvent/ 100 rags Industry average/ ‘typical’ *

Docket Item No. 0116 (1999) 

- John Clement @ Bock 

Engineered Products, Inc.

---

Centrifuging test for 

solvent recovery – not 

usage

Centrifuge solvent 

recovery: 95%, 96%, 98%, 

100%

Four test runs

Docket Item No. 0119 Aerospace/ Primarily 

disposables Use quantity 50-250 mL applied per wipe

Docket Item No. 0120 (1999) 

- Kent Moore @ MicroChem, 

L.L.C.

Printing/ Laundered towels

Solvent recovered 

from screen bottom 

drum following actual 

use

--- 4725 gallons recovered/ 

420,000 towels Screen-bottom drum

Actual use --- 6,300 gallons/ 420,000 

towels --- *

Docket Item No. 0122 (2000) 

- Ed :Jackson @ Fierro 

Technologies

Various sites: Paint/ ink 

manufacturing; auto 

manufacturing; general 

machining

Actual use --- 20-22 gram/wipe --- *

Docket Item No. 0123 (1999) 

- Serigraph Printing wipes

Centrifuging for 

solvent recovery 

following actual use

--- 28.5 lb solvent recovered/ 

902 wipes (22 lb wipes) *

Docket Item No. 0124 (2000) 

- Christine Clement @ Bock 

Engineered Products, Inc.

Laundered shop towels

Centrifuging for 

solvent recovery 

following actual use

---

2.5-3.5 gallons of solvent 

recovered for every 255 

wipes centrifuged.

. *

Docket Item No. 0125 (2001) 

- Joe Mattson @ 

ITU/AbsorbTech

Laundered shop towels Typical/ estimate for 

solvent recovery firm --- 4.15 gallons/ 100 lb load ---

Cal/EPA, 2004 - Cal/EPA, 

2004 - PIP, CA

Lithographic printing

Solvent use quantity 

for cleanup (in 

conjunction with 

wipes)

---

5 gal/ month

Cal/EPA, 2004 - Press Ink, ---

CA 23 gal/month

Cal/EPA, 2004 - Castle 104 gal/month
62

Reference Type of Towel/ 

Wipe & Industry

Activity Weight of Dry 

Towel/ Wipe

Weight of Wet 

Towel/ Wipe

Notes *C*

Press, CA

Cal/EPA, 2004 - Nelson, CA 75 gal/ month

Cal/EPA, 2004 - DOT. CA 2600 gal/ yr

Cal/EPA, 2004 - Paluch, CA 80 gal/ yr

Cal/EPA, 2004 - Donnelley, 

CA 13,000 gal/ yr

EPA, 1997 Printing (Laundered) Application Solvent use: 36 gal/month

Application rate of 51 

g/wipe estimated using 

towel use in Table A-1

*

EPA, 1997 Printing (Disposal + 

Laundered) Application Solvent use: 12 gal/month

Application rate of 27 

g/wipe estimated using 

laundered+disposed towel 

use in Table A-1

*

EPA, 1997 Auto body (Disposal + 

Laundered) Application Solvent use: 37.5 

gal/month

Application rate of 57 

g/wipe estimated using 

laundered+disposed towel 

use in Table A-1

*

EPA, 1997 Auto maintenance 

(Disposal + Laundered) Application Solvent use: 15 gal/month

Application rate of 28 

g/wipe estimated using 

laundered+disposed towel 

use in Table A-1

*

EPA, 1997 Circuit board (Disposed) Application Solvent use: 5 gal/month

Application rate of 9 

g/wipe estimated using 

towel use in Table A-1

*

EPA, 1997 Aerospace instr. Mfg 

(Disposal + Laundered) Application Solvent use: 20 gal/month

Application rate of 131 

g/wipe estimated using 

laundered+disposed towel 

use in Table A-1

*

The final column (labeled “*C*”) identifies if the data were used in the evaluation; a “*” indicates the data were used.

All numbered docket items are referenced from the RCRA-2003-0004 Docket.
63

Additional Miscellaneous Model Input Data from Comments and Other Background Sources in 

Docket RCRA-2003-0004

Docket item No. 0109: Kimberly Clark survey of sites in various SIC codes, 1997 [survey details 

such as sample size and SIC codes covered are not available]:

• Laundered towels with hazardous solvents

o Percent of survey sites using laundered towels: 100% (a condition of the survey)

o Percent of survey sites that use hazardous solvents in conjunction with laundered shop 

towels: 59% (note: ‘hazardous solvent’ not defined)

o Percent of laundered shop towels which actually contact the hazardous solvent: 38%

o Overall percent of laundered shop towels which contact hazardous solvents, at sites 

using laundered towels: 22% (i.e., 59% x 38%).

• Disposable towels/ wipes with hazardous solvents

o Percent of survey sites using disposable towels/ wipes: not given

o Percent of sites using disposable towels/ wipes that use hazardous solvents in 

conjunction with disposable towels/ wipes: 47% (note: ‘hazardous solvent’ not defined)

o Percent of disposable towels/ wipes which actually contact the hazardous solvent: 30%

o Overall percent of laundered shop towels which contact hazardous solvents, at sites 

using laundered towels: 14% (i.e., 47% x 30%).

Docket item No. 0110: Letter by Kimberly Clark, 1997

• Non-printing

o Percent of laundered shop towel users (non-printing) that use RCRA solvents in 

conjunction with laundered shop towels: 15.8% (note: ‘RCRA solvent’ not defined)

o Percent of laundered shop towels which actually contact the RCRA solvent (nonprinting): 22%

o Overall percent of laundered shop towels which contact hazardous solvents, at nonprinting sites using laundered towels: 3.5% (i.e., 15.8% x 22%).

• Printing

o Percent of laundered shop towel users (printing) that use RCRA solvents in conjunction 

with laundered shop towels: 8.2% (note: ‘RCRA solvent’ not defined)

o Percent of laundered shop towels which actually contact the RCRA solvent (printing): 

80%

o Overall percent of laundered shop towels which contact hazardous solvents, at printing 

sites using laundered towels: 6.6% (i.e., 8.2% x 80%).

Docket item No. 0112: Letter from Maratek, 1998

• The printing industry purchases 10 to 15 million gallons of solvents annually. This does not 

include the publishing industry.

o 10% of this is listed hazardous solvents

o 80% are characteristically hazardous

o 10% are nonhazardous
64

Appendix B. Data Values Used in Disposables Analysis
65

Exhibit B-1. Use of Disposable Wipes in Five Industry Sectors

Reference Apply Wipe Usage to Sub-sector Towels/ year

Gradient – Beyer, 2003 Auto Repair 7,692

EPA 1997 Auto Repair 24,000

EPA 1997 Auto Repair 7,200

No. 0118 Electronics 450,000

EPA 1997 Electronics 24,000

No. 0122 Other manufacturing 228,000

No. 0052 Other manufacturing 10,000

No. 0102 Other manufacturing 165,000

Gradient – Beyer, 2003 Other Manufacturing 7,692

Gradient – Beyer, 2003 Other Manufacturing 10,000

Gradient – Beyer, 2003 Other Manufacturing 15,000

Gradient – Beyer, 2003 Other Manufacturing 25,000

Gradient – Beyer, 2003 Other Manufacturing 5,000

Gradient – Beyer, 2003 Other Manufacturing 30,000

Gradient – Beyer, 2003 Other Manufacturing 12,500

Gradient – Beyer, 2003 Other Manufacturing 75,000

Gradient – Beyer, 2003 Other Manufacturing 50,000

Gradient – Beyer, 2003 Other Manufacturing 225,000

Gradient – Beyer, 2003 Other Manufacturing 50,000

Gradient – Beyer, 2003 Other Manufacturing 50,000

Gradient – Beyer, 2003 Other Manufacturing 10,000

Gradient – Beyer, 2003 Other Manufacturing 400,000

No. 0387 Other Manufacturing 15,500

No. 0387 Other Manufacturing 11,350

EPA, 2006 Site visit Other Manufacturing 108,000

EPA, 2006 Site visit Other Manufacturing 12,500

EPA, 2006 Site visit Other Manufacturing 12,000

EPA 1997 Other manufacturing 480

No. 0106 Printing 3,000

No. 0106 Printing 30,000

No. 0107 Printing 3,900

No. 0107 Printing 11,400

No. 0107 Printing 22,500

No. 0113 Printing 158,200

No. 0113 Printing 60,850

No. 0113 Printing 41,350

No. 0115 Printing 600,000

No. 0120 Printing 420,000

Gradient – Beyer, 2003 Printing 400,000

No. 0130 Printing 350,000

EPA, 2006 Site visit Printing 75,000

EPA, 2006 Site visit Printing 5,400

PNEAC, 1996 Printing 210,000

EPA, 2006 Site visit Printing 130,000

EPA, 2006 Site visit Printing 1,500
66

Reference Apply Wipe Usage to Sub-sector Towels/ year

EPA, 2006 Site visit Printing 162,500

EPA, 2006 Site visit Printing 1,000

EPA 1997 Printing 28,800

EPA 1997 Printing 14,400

No. 0119 Transportation 7,500

No. 0119 Transportation 300,000

Gradient – Beyer, 2003 Transportation 100,000

Gradient – Beyer, 2003 Transportation 17,500

Gradient – Beyer, 2003 Transportation 50,000

Gradient – Beyer, 2003 Transportation 18,750

Gradient – Beyer, 2003 Transportation 37,500

Gradient – Beyer, 2003 Transportation 25,000

No. 0099 Transportation 270,000

No. 0099 Transportation 67,000

No. 0099 Transportation 11,000

No. 0099 Transportation 41,000

EPA 1997 Transportation 14,400

All numbered docket items are referenced from the RCRA-2003-0004 Docket.

Exhibit B-2. Solvent Application Rates on Disposable Wipes in Five Industry Sectors

Reference Industry Grams/wipe Note

No. 0118 Electronics 7 Estimated from application rate using 

evaporation assumption

EPA 1997 Electronics 6 Estimated from application rate using 

evaporation assumption

No. 0110 General 10

No. 0127 General 56

EPA 2006 Site 

visit General 11 Estimated from application rate using evaporation assumption

No. 0115 General 34

No. 0122 General 21

No. 0124 General 44

EPA 1997 General 97 Estimated from application rate using 

evaporation assumption

EPA 2006 Site 

visit Printing 86

EPA 2006 Site 

visit Printing 9 Estimated from application rate using evaporation assumption

EPA 2006 Site 

visit Printing 28

EPA 2006 Site 

visit Printing 43

No. 0108 Printing 48

No. 0120 Printing 51

No. 0123 Printing 14

EPA 1997 Printing 38 Estimated from application rate using 
67

Reference Industry Grams/wipe Note

evaporation assumption

EPA 1997 Printing 20 Estimated from application rate using 

evaporation assumption

EPA 1997 Auto Repair 42 Estimated from application rate using 

evaporation assumption

EPA 1997 Auto Repair 21 Estimated from application rate using 

evaporation assumption

All numbered docket items are referenced from the RCRA-2003-0004 Docket.

Exhibit B-3. Percent of Disposable Wipes Containing Solvent Chemicals in Five Industry 

Sectors

Chemical

Range and Average for Each Sector

Data 

Sources Printing Transportation Auto Repair Electronics Other

Acetone 1, 5 16-26% 

(avg. 21%)

Same as 

printing 16-25% (avg. 21%) Same as printing

Same as auto 

repair

Benzene 2, 6 0.2-2.1% 

(avg. 1.1%) Same for all

n-Butanol 2, 6 2.1-5.4% 

(avg. 3.7%) Same for all

Chlorobenzene 3, 6 0.4-0.9% 

(avg. 0.6%) Same for all

Cresols 1, 6 0-0.3% 

(avg. 0.2%)

0.3-0.4% (avg. 

0.3%)

0.3-0.5% (avg. 

0.4%)

0.3-0.5% 

(avg. 0.4%)

0.3-0.6% 

(avg. 0.5%)

Cyclohexanone 4, 5 1.8% Same for all

1,2-

Dichlorobenzene 3, 6 0.2-0.9% (avg. 0.5%) Same for all

Ethyl acetate 2, 5 2.4-6.6% 

(avg. 4.5%) Same for all

Ethyl benzene 1, 6 5.7-26% 

(avg. 16%)

Same as 

printing

5.7-25% (avg. 

16%)

Same as 

printing

Same as auto 

repair

2-Ethoxyethanol 1, 6 0.1-20% 

(avg. 10%)

0.1-22% (avg. 

11%)

0.1-26% (avg. 

13%)

0.1-14.5% 

(avg. 7.3%)

0.1-20.5% 

(avg. 10%)

Isobutyl alcohol 2, 5 0.3-0.9% 

(avg. 0.6%) Same for all

Methanol 1, 6 10-26% 

(avg. 18%)

Same as 

printing 10-25% (avg. 18%) Same as printing

Same as auto 

repair

Methyl ethyl 

ketone 1, 6 8.4-20% (avg. 14%)

8.4-22% (avg. 

15%)

8.4-26% (avg. 

17%)

8.4-14% 

(avg. 11%)

Same as 

printing

Methyl isobutyl 

ketone 1, 6 4.2-26% (avg. 15%)

Same as 

printing

4.2-25% (avg. 

15%)

Same as 

printing

Same as auto 

repair

Methylene 

chloride 1, 6 2.1-3.5% (avg. 2.8%)

2.1-6.6% (avg. 

4.4%)

2.1-5.2% (avg. 

3.6%)

2.1-8.7% 

(avg. 5.4%)

2.1-8.0% 

(avg. 5.1%)

Tetrachloroethylene 2, 6 0.7-0.9% 

(avg. 0.8%) Same for all

Toluene 1, 6 13-20% 

(avg. 16%)

13-22% (avg. 

17%) 13-26% (avg. 19%) 13-14% (avg. 14%)

13-20% 

(avg. 17%)

1,1,2-

Trichloroethane 3, 6 0.1-0.9% (avg. 0.5%) Same for all

Trichloroethylene 2, 6 1.7-1.8% 

(avg. 1.8%) Same for all

Xylene 1, 6 12-26% 

(avg. 19%)

Same as 

printing 12-25% (avg. 19%) Same as printing

Same as auto 

repair
68

Data sources:

1. For NESHAP-identified chemicals, assume chemical use = overall F-solvent use because these will be the most 

frequently used chemicals (industry-dependent).

2. For others, use frequency from EPA Miscellaneous metal parts NESHAP. Use same percent for all industries.

3. For three chlorinated solvents, Document No. RCRA-2003-0004-0106 identifies trichloroethane is used in printing 

industry by 3% of sites. Assume similar usage for other chlorinated chemicals and other industries.

4. For cyclohexanone, use is identified in Document No. RCRA-2003-0004-0119 (aerospace) and in printing. However 

it is not widely mentioned. Use frequency data from TURA.

5. Frequency of reporting is from 2004 TURA.

6. Frequency of reporting is from 2004 TRI.

Exhibit B-4. Percent of Generators in Each of Five Industry Sectors

EA Sector Sector name NAICS Count Percent A

Printing subtotal 3,460 24%

1 Printing mfg 323 1,976

10 Publishing (printed matter) 5111 1176

11 Business Services (copy shops) 561439 308

Transportation subtotal 159 1%

7 Transportation Equip Mfg 336 159

Auto Repair subtotal 2,026 14%

12 Auto Repair & Maintenance Services 8111 2,026

Electronics subtotal 70 0.5%

6 Electronics & Computers Mfg 3344 70

Other subtotal Remaining 61%

2 Chemical & Allied Products Mfg 325 169

3 Plastics & Rubber Products Mfg 326 192

4 Fabricated Metal Products Mfg 332 764

5 Industrial Machinery & Equip Mfg 333 345

8 Furniture & Fixtures Mfg 337 275

9 Auto Dealers (Retail trade) 4411 638

13 Military Bases 92812 8

TOTAL from EA 8,106

TOTAL accounting for industries 

beyond EA

8,106/0.56 = 

14,475 100%

Data from Economic Analysis (EPA, 2003)

A. Percent calculated as percent of 14,475 (see Section 2.3.4).

Exhibit B-5. Number of Disposable Wipes Generators Per Landfill

State % Generators 

in State A

Number of 

Landfills in 

State B

Landfilled as 

% of all 

Discards C

Generators 

per 

Landfill D

Alabama 1.5% 96.94

Alaska 0.1% 96.97

Arizona 1.5% 40 100.00 12

Arkansas 0.9% 98.38

California 11.4% 153 97.52 23

Colorado 1.7% 72 100.00 8

Connecticut 1.6% 2 14.38 35

Delaware 0.2% 3 100.00 23

Florida 5.0% 54 66.58 20

Georgia 2.6% 57 98.80 15

Hawaii 0.2% 63.97

Idaho 0.5% 26 100.00 7

Illinois 5.0% 52 100.00 30
69

State % Generators 

in State A

Number of 

Landfills in 

State B

Landfilled as 

% of all 

Discards C

Generators 

per 

Landfill D

Indiana 2.5% 36 92.31 21

Iowa 1.3% 59 97.85 7

Kansas 1.1% 52 99.88 7

Kentucky 1.3% 27 99.74 15

Louisiana 1.0% 95.16

Maine 0.5% 10 70.84 12

Maryland 1.5% 31 71.43 12

Massachusetts 2.4% 21 44.11 16

Michigan 4.4% 51 91.66 25

Minnesota 2.4% 23 63.56 21

Mississippi 0.8% 17 100.00 16

Missouri 2.2% 22 99.18 31

Montana 0.4% 29 99.06 5

Nebraska 0.7% 23 100.00 10

Nevada 0.6% 23 100.00 8

New Hampshire 0.6% 8 78.50 19

New Jersey 2.9% 12 76.44 57

New Mexico 0.5% 39 100.00 5

New York 5.8% 26 65.79 45

North Carolina 3.1% 41 98.89 24

North Dakota 0.3% 14 100.00 7

Ohio 4.7% 42 100.00 35

Oklahoma 1.2% 31 91.68 12

Oregon 1.5% 28 93.36 16

Pennsylvania 4.6% 48 75.59 23

Rhode Island 0.4% 2 99.77 67

South Carolina 1.3% 18 93.39 21

South Dakota 0.4% 15 100.00 8

Tennessee 2.0% 35 100.00 18

Texas 6.4% 189 99.87 11

Utah 0.9% 34 94.87 8

Vermont 0.4% 4 87.55 24

Virginia 2.1% 60 82.33 10

Washington 2.0% 19 94.13 31

West Virginia 0.5% 18 100.00 9

Wisconsin 2.8% 36 96.15 23

Wyoming 0.2% 52 100.00 2

TOTAL 100.0% 1,654 89.65 17

Avg --> 18.9

Median --> 16

A. Distribution of 7 NAICS wipe-generating industries from 2002 Dept of Census.

Pretreatment Standards for the Industrial Laundries Point Source Category (Revised March 2000). Table 4-1. Sites for 

District of Columbia are combined with Maryland.

B. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26-43. Table 9. Data not available for 

some states.

C. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26-43. Table 3. This column represents 

the percent landfilled for discarded MSW only, versus that destined for combustion (i.e., excludes recycling). 

D. Minimum 1 generator per landfill. Accounts only for SQG/LQG generating disposable RCRA towels; accounts that 

waste is landfilled or combusted according to overall MSW percentage. Uses total of 30,380 generators.
70

Exhibit B-6. Evaporation Rates of Solvents from Wipes

Scenario % Solvent Remaining Solvent Reference 

Open Container (18-24 hours) 49 BP-308 EPA, 1997

Open Container (18-24 hours) 92 BP-308 EPA, 1997

Open Container (18-24 hours) 85 BP-303 EPA, 1997

Open Container (18-24 hours) 52 Plastic EPA, 1997

Open Container (18-24 hours) 70 Plastic EPA, 1997

Open Container (18-24 hours) 59 DX330 EPA, 1997

Open Container (18-24 hours) 75 DX330 EPA, 1997

Open Container (18-24 hours) 97 BP-308 EPA, 1997

Closed Container (18-24 hours) 106 Lacquer EPA, 1997

Closed Container (18-24 hours) 92 Lacquer EPA, 1997

Closed Container (18-24 hours) 93 Lacquer EPA, 1997

Closed Container (18-24 hours) 72 Lacquer EPA, 1997

Closed Container (18-24 hours) 99 Lacquer EPA, 1997

Closed Container (18-24 hours) 45 Lacquer EPA, 1997

Closed Container (18-24 hours) 0 MEK EPA, 1997

Closed Container (18-24 hours) 0 MEK EPA, 1997

Closed Container (18-24 hours) 0 MEK EPA, 1997

Closed Container (18-24 hours) 0 MEK EPA, 1997

Closed Container (18-24 hours) 0 MEK EPA, 1997

Average (excluding MEK) 78 %

Average (MEK only) 0 %

EPA, 1997. Use and Management Practices of Solvent Contaminated Industrial Shop Towels. Final Document. 

Exhibits 1-3a and 1-3b.

With the exception of methyl ethyl ketone (MEK), no further information is available regarding the properties or 

composition of the solvents used in EPA (1997).

The data in Exhibit B-6 labeled as ‘open container’ came from facilities where the spent wipers 

were stored in the open (e.g., on a shelf, in open containers, etc.). The data in Exhibit B-6 labeled 

as ‘closed container’ came from facilities where the spent wipers were stored in closed containers. 

All of the wipers stored in open containers retained in excess of 49% of the solvent added to the 

wiper. With the exception of data points for MEK, the same is true for wipers stored in closed 

containers. There are no clear differences between storage in open containers and closed 

containers, in terms of the percentage of solvent remaining on the wiper after 18-24 hours. The vast 

majority of the wipers retained in excess of 50% of the solvent (EPA, 1997).

All wipers used in conjunction with MEK were KimWipes. The KimWipes used with MEK 

retained 0% of the solvent; probably because of the volatility of the solvent and the relatively low 

adsorption of the wiper (EPA, 1997).
71

Appendix C. Industry-Specific Use of F-Listed Solvents – NESHAPs
72

Through its air toxics program, EPA has finalized National Emission Standards for Hazardous Air 

Pollutants (NESHAPs). EPA has investigated the use and emissions of specific chemicals when 

developing these regulations. These regulations cover many industries, which use solvents in 

conjunction with towels or wipes, and therefore the data are useful in assessing the overall use 

pattern of specific solvents. These data were used to assess solvent use patterns in various 

industries. Some of the benefits and limitations in using these data for purposes of this solvent 

wipes project include the following:

Benefits

• Useful for identifying whether or not a chemical is present in an industry.

• Based on detailed site and industry study by EPA.

• Solvents used in conjunction with wipes are a common source of investigated air emissions.

• Of the 30 F-listed solvents, the majority (21) is HAPs and likely to be assessed in detail in 

NESHAP development materials. Chemicals, which are not HAPs, include acetone, butanol, 

ethyl acetate, ethyl ether, 2-ethoxyethanol, isobutyl alcohol, pyridine, CFC-113, CFC-11, and 

methyl ethyl ketone.17

Limitations

• The source data are typically old (e.g., 1990’s) and may not reflect current use patterns.

• Sources of air emissions include operations, which do not necessarily involve the use of wipes 

in conjunction with solvents. This is particularly true of surface coating NESHAPs, where the 

majority of emissions are likely the result of painting where wipes are not typically used. 

Applications of wipes in surface coating operations are most likely in surface preparation where 

solvent use and emissions are smaller.

• Data and regulations are applicable to large facilities. These practices may or may not reflect all 

sites.

Chemicals representing the most significant HAP emissions were identified from the NESHAPs 

development materials for 11 industries, predominantly as indicated in the Federal Register notices. 

Nine F-listed chemicals were identified from these materials as follows:

• Cresols

• Ethylbenzene

• Glycol ethers (does not identify individual glycol ethers such as 2-ethoxyethanol)

• Methanol

• Methylene chloride

• Methyl ethyl ketone

• Methyl isobutyl ketone (MIBK),

• Toluene

• Xylene

The above list represents the most predominant chemicals or the chemicals emitted in the greatest 

amounts. Additional chemicals, including non-HAP chemicals, may be emitted from these 

industries as well.

Industry: Semiconductor manufacturing

 17 Methyl ethyl ketone was delisted as a HAP in 2004 but NESHAPs developed prior to 2004 address this chemical. 

The list of HAPs includes glycol ethers as a category but does not identify individual glycol ethers such as 2-

ethoxyethanol.
73

Organic HAPs emitted from semiconductor manufacturing facilities include methanol, glycol 

ethers, and xylene. Five chemicals (i.e., these three plus two additional chemicals, which are not Flisted solvents) are estimated to comprise 90 percent of the total nationwide HAP emissions of 636 

tons per year in this industry (67 FR 30851).

Aerospace

Organic HAPs emitted from aerospace manufacturing and rework facilities include methylene 

chloride, toluene, xylene, methyl ethyl ketone, and glycol ethers (60 FR 45948).

Automotive Assembly Surface Coating

Organic HAPs emitted from surface coating of automobiles and light-duty truck manufacturing 

(assembly plants) are toluene, xylene, glycol ethers, methyl ethyl ketone (MEK), methyl isobutyl 

ketone (MIBK), ethylbenzene, and methanol. These chemicals comprise over 95 percent of 

nationwide HAP emissions from the source category. Emissions are primarily associated with 

coating and drying. Surface preparation sources were estimated to be smaller by comparison (69 

FR 22602).

Appliance Surface Coating

Organic HAPs emitted from surface coating of appliance manufacturing are toluene, xylene, glycol 

ethers, and methyl ethyl ketone (MEK). Five chemicals (i.e., these four plus one additional 

chemical that is not an F-listed solvent) are estimated to comprise 80 percent of the total nationwide 

HAP emissions (baseline 2,839 tons) in this source category (67 FR 48253).

Metal Can Surface Coating

Primary organic HAPs emitted from surface coating of metal can manufacturing are xylene, glycol 

ethers, methyl isobutyl ketone (MIBK), and methyl ethyl ketone (MEK). Other organic HAPs 

identified include ethyl benzene and toluene. Total baseline nationwide HAP emissions are 9,600 

tons (includes contributions from these and other chemicals which are not F-listed solvents) in this 

source category (68 FR 64431).

Metal Coil Surface Coating

Organic HAPs emitted from surface coating of metal coil are toluene, xylene, glycol ethers, and 

methyl ethyl ketone (MEK) (67 FR 39793).

Metal Furniture Surface Coating

Organic HAPs emitted from surface coating of metal furniture manufacturing are toluene, xylene, 

glycol ethers, ethyl benzene, and methyl ethyl ketone (MEK) (68 FR 28605).
74

Surface Coating of Miscellaneous Metal Parts and Products

Organic HAPs emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl 

ketone (MEK), cresols, glycol ethers, and methyl isobutyl ketone (MIBK). These chemicals, plus 

others identified which are not F-listed solvents, are estimated to comprise 90 percent of the total 

nationwide HAP emissions in this source category. The majority of emissions are associated with 

the application, drying, and curing of coatings; smaller emissions are associated with cleaning 

operations (69 FR 130).

The source of cresol is identified in a 1998 Background Document as a solvent for a coating used in 

the production of magnet wire, a product made by up to 30 large sites (EPA, 2002).

Printing and Publishing

Organic HAPs emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl 

ketone (MEK), methanol, glycol ethers, and methyl isobutyl ketone (MIBK). Total baseline 

nationwide HAP emissions are 23,900 tons in this source category. The source category includes 

rotogravure and flexographic printing (61 FR 27132).

Ship Building and Ship Repair (Surface Coating)

Organic HAPs emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl 

ketone (MEK), glycol ethers, and methyl isobutyl ketone (MIBK). Total baseline nationwide HAP 

emissions are 1,500 tons per year in this source category (60 FR 64330).

Wood Furniture (Surface Coating)

Organic HAPs emitted from this source category are toluene, xylene, methanol, methyl ethyl ketone 

(MEK), glycol ethers, and methyl isobutyl ketone (MIBK). Total baseline nationwide HAP 

emissions are 1,500 tons per year in this source category (60 FR 62930).
75

Appendix D. 2003 Biennial Report Data
76

Introduction, Purpose, and General Limitations

The Hazardous Waste Report (or Biennial Report) is completed by certain hazardous waste 

generators and hazardous waste treatment, storage, or disposal facilities. Data collection is done on 

a two-year cycle as required by Sections 3002 and 3004 of the Resource Conservation and 

Recovery Act (RCRA). The information is collected via a mechanism known as the Hazardous 

Waste Report for the required reporting year. Facilities provide their completed forms to the state 

from which EPA creates a national database. As of August 2006, the most recent national database 

is for the 2003 reporting year.

Facilities are required to report if they meet the definition of a RCRA Large Quantity Generator 

(LQG) during the reporting year (e.g., the site generated at least 2,200 lbs (1,000 kg) of RCRA 

hazardous waste in any single calendar month), or they are a TSD facility (treated, stored, or 

disposed of RCRA hazardous wastes). Some states have additional requirements or, for example, 

require small quantity generators to report as well. These state-by-state variances are reflected in 

the national database.

The national database for the 2003 reporting year is available from an EPA ftp site.18 For this 

project, the June 10, 2005 versions of the files for the 2003 reporting year were used.

Principal advantages and limitations of the 2003 Biennial Report as they relate to this project are as 

follows:

• The biennial report is only required (at the federal level) for LQGs.

• For those facilities required to report, the facility must report all of its hazardous wastes, 

regardless of quantity. For example, if an LQG facility generates 1 pound of spent F001 solvent 

it must report this waste. A separate waste report form (Form GM) is completed for each 

generated RCRA hazardous waste. As a result, a single facility will typically report the 

generation of many different hazardous wastes. However, facilities are permitted to combine 

and report similar hazardous wastes on Form GM. Facilities generating solvent-contaminated 

wipes, for example, may combine reporting with similar material such as adsorbents, PPE, 

filters, etc.

• Only hazardous wastes are reported. Generators will not report shop towels sent to a laundry if 

they are not shipped as a hazardous waste. Generators will not report disposable wipes placed 

in the general trash if they are not shipped as a hazardous waste.

• Industrial laundries are not hazardous waste TSDs and therefore will not report quantities of 

shop towels received from customers. However, they may be generators of hazardous wastes 

such as from (1) co-located dry cleaning operations; (2) treatment residues, which are 

characteristically hazardous.

• In 2003, the Hazardous Waste Report used the NAICS industry classification. This provides 

consistency with U.S. Census data and the solvent wipe economic analysis (EPA, 2003).

 18 ftp://ftp.epa.gov/rcrainfodata/ar/br_2003/
77

2003 Biennial Report Fields Obtained

EPA’s biennial reporting database contains approximately 100 different fields divided among more 

than a dozen individual tables. The available fields and tables were carefully reviewed to obtain 

data likely to be relevant. Fields from three separate tables were obtained to generate relevant data 

for targeted facilities, using several Microsoft Access queries and the source files from the EPA ftp 

site. These fields are identified in Exhibit D-1.

Throughout the Microsoft Access process, strict attention was made to identify consistent record 

counts when combining records from different tables, etc. This provided assurance that duplicate 

records were not inadvertently added or other records inadvertently deleted.

Exhibit D-1. Data Extracted for Biennial Report Evaluation

File Field Name Description Source File Name

RCRA ID Facility RCRA ID Number USSI6, USGM1, USGM2

Handler Name Facility Name USSI1

NAICS Code Facility NAICS Code USSI6

Page Number (used for internal linking only) USGM1, USGM2

Form Code Identifies Waste Type USGM1

Source Code Identifies Waste Source USGM1

Qty Quantity Reported by Facility USGM1

UOM Unit of Measure for Reported Quantity USGM1

Density Density or Relative Density of Waste Given 

When UOM is Gallons or Specific Gravity

USGM1

Density UOM Unit of Measure for Density USGM1

Waste Desc A user-provided description of the Waste USGM1

Notes Any additional information provided by the 

facility

USGM1

HW Codes Waste Code (As many as needed to identify 

each waste)

USGM2

Summary of Results – Shop Towel Generators

The following process was used to assess solvent wipes generators using Biennial Report data:

• All reporting facilities must report their NAICS code. Data was evaluated for generators in 

those NAICS industry classifications used in the economic analysis. Relevant NAICS codes are 

as follows:

o 323: Printing Manufacturing

o 325: Chemical & Allied Products Manufacturing

o 326: Plastics & Rubber Products Manufacturing

o 332: Fabricated Metal Products Manufacturing

o 333: Industrial Machinery & Equipment Manufacturing

o 3344: Electronics & Computers Manufacturing

o 336: Transportation Equipment Manufacturing

o 337: Furniture & Fixtures Manufacturing

o 4411: Auto Dealers (retail trade)
78

o 5111: Publishing (printed matter)

o 561439: Business Services (copy shops)

o 8111: Auto Repair & Maintenance Services

o 92812: Military Bases

A facility may report more than one NAICS code and more than one waste code. Therefore, 

reported percentages will not be additive to 100%. This factor does not reduce the accuracy of 

the data in any way, but it must be considered when comparing data between different NAICS 

codes or waste types.

• The generation of F001 to F005 hazardous wastes in the above-targeted NAICS code industries 

was assessed. This identifies the percentage of facilities that generate F001 to F005 wastes 

within any chosen industry.

• The generation of F001 to F005 wastes that result from the use of contaminated wipes or similar 

materials was assessed. This shows the percentage of facilities within each industry that report 

as generators of solvent contaminated wipes (or similar waste). A facility indicates the general 

physical properties and origin of the waste using form codes and source codes. The most 

relevant indicator is form code W002: Contaminated debris: paper, clothing, rags, wood, empty 

fiber or plastic containers, glass, piping, other solids (usually from construction, demolition, 

cleaning, or remediation).

The form and source code data, while accurate and representative of the Biennial Report, contains 

uncertainties regarding how facilities define their wastes at the time of generation. This information 

is somewhat qualitative and descriptive and therefore different facilities may describe similar 

wastes differently. In addition, heterogeneous waste shipments containing solvent-contaminated 

rags may not be described using the above codes. Because of this uncertainty, data analysis has 

been conducted with and without consideration of these form codes.

Information for a total of 18,786 generators is available in the 2003 biennial report. Of these:

• 9,048 facilities (48%) have an NAICS code corresponding to one of the 13 NAICS codes 

identified above (and used in the economic analysis).

• 9,877 facilities (53%) report the generation of F001-F005 wastes.

• 3,813 facilities (20%) report the generation of wastes with form code W002 (spent rags and 

other materials).

Exhibits D-2 and D-3 summarize the generated hazardous wastes. Exhibits D-4 and D-5 provide 

similar information as Exhibits D-2 and D-3 for specific industries.

Exhibit D-2. Number of Sites Generating Wastes in 2003 Biennial Report

All wastes Any of F001 -

F005

F001 F002 F003 F004 F005

Sites in 13 

NAICS Only

9,048 5,660 (63%) 848 

(9%)

1,420 

(16%)

4,812 

(53%)

98 (1%) 3,847 

(43%)

All BR Sites 18,786 9,877 (53%) 1,784 

(9%)

3,338 

(18%)

8,325 

(44%)

363 

(2%)

6,689 

(36%)

Percentages are of the number of sites reporting all wastes (e.g., 9,048 sites in the 13 NAICS codes).
79

Exhibit D-3. Number of Sites Generating Shop Towels and Similar Wastes (Form Code 

W002) in 2003 Biennial Report

All 

wastes

Any 

W002 

waste

Any of 

F001 -

F005

F001 F002 F003 F004 F005

Sites in 13 

NAICS Only

9,048 1,873 1,504 

(17%)

282 

(3%)

545 

(6%)

1,328 

(15%)

48 (1%) 1,131 

(13%)

All BR Sites 18,786 3,813 2,754 

(15%)

657 

(3%)

1,229 

(7%)

2,434 

(13%)

174 

(1%)

2,102 

(11%)

Percentages are of the number of sites reporting all wastes (e.g., 9,048 sites in the 13 NAICS codes).

Form Code W002 includes, but is not limited to, shop towels and wipes.

Exhibit D-4. Number of Sites Generating Wastes in 2003 Biennial Report for Specific 

Industries

INDUSTRY NAICS Any 

waste

Any of F001 

- F005 F001 F002 F003 F004 F005

Printing Manufacturing 323 475 239 10 20 203 0 153

Chemical & Allied Products Manufacturing 325 2,669 1,918 229 747 1,718 63 1,328

Plastics & Rubber Products Manufacturing 326 634 491 56 95 424 5 309

Fabricated Metal Products Manufacturing 332 2,534 1,118 256 179 831 11 715

Industrial Machinery & Equipment Manufacturing 333 571 375 63 89 294 2 259

Electronics & Computers Manufacturing 3344 701 390 77 113 332 6 188

Transportation Equipment Manufacturing 336 1,286 994 177 219 874 12 739

Furniture & Fixtures Manufacturing 337 286 251 2 6 239 0 217

Auto Dealers (retail trade) 4411 42 27 3 1 21 0 23

Publishing (printed matter) 5111 24 4 0 2 3 0 3

Business Services (copy shops) 561439 15 11 7 8 11 0 10

Auto Repair & Maintenance Services 8111 119 64 7 11 54 1 56

Military Bases 92812 3 2 0 1 1 0 1

Any of above 13 NAICS 9,048 5,660 848 1,420 4,812 98 3,847

Total (all facilities in Biennial Report) 18,786 9,877 1,784 3,338 8,325 363 6,689

Overall

Printing 7% 12% 85% 0% 65%

Transportation 18% 22% 88% 1% 74%

Auto repair 11% 17% 84% 2% 88%

Electronics 20% 29% 85% 2% 48%

Other 15% 27% 84% 2% 68%

Numbers in rows and columns are not additive because a site can report multiple NAICS codes or multiple waste types.
80

Exhibit D-5. Number of Sites Generating Shop Towels and Similar Wastes (Form Code 

W002) in 2003 Biennial Report for Specific Industries

INDUSTRY NAICS

All 

wastes

Any 

W002 

waste

Any of 

F001 -

F005 F001 F002 F003 F004 F005

Printing Manufacturing 323 475 71 11% 0% 2% 10% 0% 8%

Chemical & Allied Products 

Manufacturing 325 2,669 641 20% 3% 10% 19% 1% 15%

Plastics & Rubber Products 

Manufacturing 326 634 153 22% 3% 5% 19% 0% 17%

Fabricated Metal Products 

Manufacturing 332 2,534 379 10% 3% 2% 8% 0% 7%

Industrial Machinery & Equipment 

Manufacturing 333 571 107 15% 2% 5% 13% 0% 11%

Electronics & Computers 

Manufacturing 3344 701 195 22% 4% 7% 19% 0% 12%

Transportation Equipment 

Manufacturing 336 1,286 379 26% 7% 9% 23% 1% 22%

Furniture & Fixtures Manufacturing 337 286 28 9% 0% 0% 8% 0% 8%

Auto Dealers (retail trade) 4411 42 1 2% 0% 0% 2% 0% 2%

Publishing (printed matter) 5111 24 1 4% 0% 4% 4% 0% 4%

Business Services (copy shops) 561439 15 0 NA NA NA NA NA NA

Auto Repair & Maintenance Services 8111 119 3 1% 0% 1% 1% 0% 1%

Military Bases 92812 3 1 0% 0% 0% 33% 0% 33%

Any of above 13 NAICS 9,048 1,873 17% 3% 6% 15% 1% 13%

Total (all facilities in Biennial Report) 18,786 3,813 15% 3% 7% 13% 1% 11%

Percentages are of the number of sites reporting all wastes (e.g., 475 sites in NAICS code 323).

Form Code W002 includes, but is not limited to, shop towels and wipes.

Conclusions regarding the use of the Biennial Report for evaluating towels and wipes are as 

follows:

• The Biennial Report primarily collects data for large quantity generators (LQGs) of hazardous 

wastes. The total number of sites in each industry is greater than this. The Biennial Report data 

are most useful for industries that have a relatively high number of LQGs (e.g., NAICS 336 

Transportation Equipment Manufacturing). The data are less useful for industries, which 

predominantly are not LQGs (e.g., NAICS 8111 Auto Repair & Maintenance Services).

• The statistics for the 13 NAICS codes (taken together) are similar to all Biennial Report 

generators. This is shown in Exhibits D-2 and D-3.

• Exhibits D-2 and D-4 are useful for assessing the use of certain types of solvents. Exhibit D-2 

shows that F003 wastes are most common while F004 wastes are least common. By extension, 

it is expected that the use of F003 solvents is much more prevalent than the use of F004 

solvents. This information does not identify if disposable, reusable, or neither type of towels or 

wipes is used with these solvents.

• Waste generation for individual NAICS codes have significant variation. For example, Exhibit 

D-4 shows that 68% of sites in transportation equipment manufacturing (NAICS 336) generate 

F003 wastes, while 33% of sites in Fabricated Metal Products Manufacturing (NAICS 332) 

generate F003 wastes.

• There is high uncertainty with the use of data in Exhibits D-3 and D-5 as to whether they 

represent shop towels. Facilities report the form code most closely approximating the physical 
81

state of the waste; therefore, there is some subjectivity on the part of the reporting facility. The 

form code category W002 also includes many items, which are not shop towels, or wipes. 

Finally, facilities may report shipments of shop towels or wipes as some other form code.

• Shop towels or wipes used with F001 to F005 solvents would not be reflected in the Biennial 

Report data if: (1) they are disposed as nonhazardous waste; or (2) they are sent to an industrial 

laundry.

Summary of Results – Laundries

The Biennial Report data were used to assess laundries processing shop towels. Information for a 

total of 18,786 generators is available in the 2003 biennial report. Of these, 45 facilities have an 

NAICS code corresponding to laundries:

• 81233: Linen and Uniform Supply

• 812331: Linen supply

• 812332: Industrial launderers

Not all of these sites process shop towels. It is difficult to identify comprehensively which sites 

accept shop towels and which do not. Instead, each waste generated by the above facilities was 

reviewed to estimate wastes associated with shop towel use. These 45 facilities reported generating 

128 wastes:

• Wastes retained for assessment (a total of 29 wastes from 19 facilities were retained):

o Some wastes were described (by the generating sites) as solvent recovered from shop 

towel processing. These are relevant to the assessment.

o Some wastes were identified as wastewater treatment sludges (source code G23)19. 

These are relevant to the assessment even though shop towels may or may not be 

processed at this particular site.

• Wastes eliminated from assessment (a total of 99 wastes were eliminated):

o Some wastes are described as spent tetrachloroethylene. These are most likely 

associated with dry cleaning operations and were eliminated from assessment.

o Some wastes have no description or are designated in some other manner. These were 

eliminated from the assessment.

Exhibit D-6 summarizes the hazardous wastes reported as generated by laundries.

Exhibit D-6. Biennial Report Data for Laundries

Waste Type Number of Sites 

Generating

Qty Generated, 

lb

Avg Per Site, 

lb

WWT Sludge 17 10,141,935 600,000

Solvent recovered from rags 2 305,047 150,000

 19 Source Code G23: Wastewater treatment (sludge, filter cake, etc., including wastes from treatment before discharge 

by NPDES or POTW or by UIC disposal).
82

Appendix E. Federal and State Right-to-Know Data for Evaluating Solvent Use
83

The availability of Toxics Release Inventory (TRI) data was reviewed for each of the F-listed 

chemicals. Data from one state program, Massachusetts, were also investigated because this state 

provides data for additional chemicals and industries, which are not addressed in the federal TRI 

program. A comprehensive, state-by-state evaluation was not performed to identify other states 

with requirements beyond those at the federal level.20

TRI data is available from EPA’s Envirofacts website (www.epa.gov/enviro/). Massachusetts data 

(Toxics Use Reduction Act or TURA) are found at 

http://www.mass.gov/dep/toxics/priorities/priorities.htm#tura. As of August 2006, the most recent 

data for both programs are for 2004. In neither case was historical data assessed.

Data was evaluated with respect to the generating industries (e.g., facilities that may generate 

solvent wipes/ towels) as well as industrial laundries (e.g., chemicals received or discharged by 

industrial laundries). 

Data Relevant to Generators

The primary purpose of this review was to identify the frequency that chemicals are reported by 

targeted industries (i.e., industries most likely to generate solvent towels or wipes). Quantitative 

data (e.g., pounds of chemicals) were not evaluated because the use or source of the chemical at the 

site is not known. For example, a facility may report that 500 pounds of a TRI chemical is sent to a 

landfill. It is not possible to estimate reasonably the types of wastes, which contribute to this total, 

or whether solvent-contaminated wipes are in fact generated at all.

What is the universe of affected industries?

Neither TRI nor TURA require facilities with less than 10 full-time employees to report. Facilities 

are subject to TRI reporting if they have a primary Standard Industrial Classification (SIC) code of 

20 to 39 (manufacturing), 7389 (facilities primarily engaged in solvents recovery services on a 

contract or fee basis), and certain other industries such as mining, electric utilities, hazardous waste 

management facilities, and oil and chemical terminals. TURA regulates a broader range of facilities 

(i.e., SIC codes) than EPCRA. Facilities conducting activities described in the following SIC codes 

must report: 10 – 14, 20 – 39, 40, 44 – 51, 72, 73, 75, and 76. Occasionally, facilities outside these 

SIC codes report to TRI or TURA on a voluntary basis.

Exhibit E-1 summarizes the TRI and TURA reporting requirements for 13 targeted industries (as 

evaluated in EPA, 2003). 

 20 New Jersey’s pollution prevention reporting requirements provide some additional information beyond federal 

requirements with respect to chemical use quantities. The industries and chemicals covered are the same as the federal 

program. The New Jersey information was not reviewed, in part, due to expected difficulties of relating the available 

information to solvent and towel/ wipe use.
84

Exhibit E-1. Coverage of Industries Using Solvent Wipes/ Shop Towels in TRI and State 

Programs

INDUSTRY NAICS SIC Subject to 

TRI?

Subject to 

TURA?

Printing Manufacturing 323 275 to 279 Yes Yes

Chemical & Allied Products 

Manufacturing 325 280 Yes Yes

Plastics & Rubber Products 

Manufacturing 326 300 Yes Yes

Fabricated Metal Products 

Manufacturing 332 340 Yes Yes

Industrial Machinery & 

Equipment Manufacturing 333 352 to 356 Yes Yes

Electronics & Computers 

Manufacturing 3344 367 Yes Yes

Transportation Equipment 

Manufacturing 336 370 Yes Yes

Furniture & Fixtures 

Manufacturing 337 250 Yes Yes

Auto Dealers (retail trade) 4411 5511 + 5521 No No

Publishing (printed matter) 5111 271 to 274 Yes Yes

Business Services (copy shops) 561439 7334 No Yes

Auto Repair & Maintenance 

Services 8111 753 No Yes

Military Bases 92812 9721 No No

Total 9 of 13 11 of 13

NAICS codes and SIC equivalents are from Economic Analysis (EPA, 2003), page 70.

What chemicals are required to be reported?

Exhibit E-2 identifies the chemicals required for TRI and Massachusetts reporting, with respect to 

the F-listed solvents. Starting in Reporting Year 2004, methyl ethyl ketone was removed from the 

list of TRI reportable chemicals. Reporting for this chemical was required for reporting year 2003 

and earlier. For reference, Exhibit E-2 includes Hazardous Air Pollutant (HAP) designations. Of 

the 30 F-listed chemicals, 25 are subject to TRI reporting and all are subject to Massachusetts 

reporting.

For TRI and for Massachusetts, chemical reporting is required only if the facility manufactures,

processes, or otherwise uses the chemical in quantities greater than the threshold during the year. 

Most chemicals used as solvents would be subject to the 10,000 pounds otherwise use threshold. 

This is equivalent to the use of approximately two 55-gallon drums of solvent per month, in the case 

of a pure solvent chemical. Only chemicals used over the threshold quantity are required to be 

reported. In the case of a solvent mixture containing various individual chemicals (such as a solvent 

containing 10 percent toluene), calculations and reporting is conducted for each chemical rather 

than for the mixture as a whole.
85

Exhibit E-2. Evaluation of F-Listed Solvents in TRI and State Programs

CAS No. Constituent EPRCA Status MA Status HAP Status

67-64-1 Acetone Reportable

71-43-2 Benzene Reportable Reportable HAP

71-36-3 Butanol Reportable Reportable

75-15-0 Carbon disulfide Reportable Reportable HAP

56-23-5 Carbon tetrachloride Reportable Reportable HAP

108-90-7 Chlorobenzene Reportable Reportable HAP

108-94-1 Cyclohexanone Reportable HAP

1319-77-3 Cresols Reportable Reportable HAP

75-71-8 Dichlorodifluoromethane Reportable Reportable HAP

95-50-1 1,2-Dichlorobenzene Reportable Reportable HAP

141-78-6 Ethyl acetate Reportable

100-41-4 Ethyl benzene Reportable Reportable HAP

60-29-7 Ethyl ether Reportable

110-80-5 2-Ethoxyethanol Reportable Reportable

78-83-1 Isobutyl alcohol Reportable

67-56-1 Methanol Reportable Reportable HAP

78-93-3 Methyl ethyl ketone * Reportable *

108-10-1 Methyl isobutyl ketone Reportable Reportable HAP

75-09-2 Methylene chloride Reportable Reportable HAP

98-95-3 Nitrobenzene Reportable Reportable HAP

79-46-9 2-Nitropropane Reportable Reportable HAP

110-86-1 Pyridine Reportable Reportable

127-18-4 Tetrachloroethylene Reportable Reportable HAP

108-88-3 Toluene Reportable Reportable HAP

71-55-6 1,1,1-Trichloroethane Reportable Reportable HAP

79-00-5 1,1,2-Trichloroethane Reportable Reportable HAP

79-01-6 Trichloroethylene Reportable Reportable HAP

76-13-1 1,1,2-

Trichlorotrifluoroethane

Reportable Reportable

75-69-4 Trichlorofluoromethane Reportable Reportable

1330-20-7 Xylenes (total) Reportable Reportable HAP

* Effective for the 2004 reporting year, methyl ethyl ketone was removed from the EPCRA and HAP lists.

Results

Exhibit E-3 summarizes the reporting results for each of the 30 chemicals from the 2004 TURA and 

TRI data. The purpose of Exhibit E-3 is to show the prevalence of reporting across the 13 different 

industries as identified in Exhibit E-1. This table does not identify how the chemicals are used by 

the facilities in the industries.
86

Exhibit E-3. Evaluation of F-Listed Solvents in EPCRA and State Programs

Chemical Reporting in 2004 TRI Reporting in 2004 TURA

# of industries represented

# of sites in 13 

industries

# of industries 

represented

# of sites in 13 

industries

Any of 30 chemicals in 

13 targeted industries 10 of 13 3,904 sites 6 of 13 65 sites

Acetone N/A --- 6 of 13 35 sites (54%)

Benzene 5 of 13 286 sites (7%) 0 of 13 0

Butanol 7 of 13 705 sites (18%) 3 of 13 4 sites (6%)

Carbon disulfide 2 of 13 (Chemical, 

Plastics) 69 sites (2%) 0 of 13 0

Carbon tetrachloride 1 of 13 (Chemical) 33 sites (0.8%) 0 of 13 0

Chlorobenzene 2 of 13 (Chemical, 

Electronics) 51 sites (1.3%) 1 of 13 (Chemical) 1 site (2%)

Cyclohexanone N/A --- 1 of 13 (Chemical) 4 sites (6%)

Cresols 2 of 13 (Chemical, 

Plastics) 44 sites (1.1%) 0 of 13 0

Dichlorodifluoromethan

e 1 of 13 (Chemical) 20 sites (0.5%) 1 of 13 (Chemical) 1 site (2%)

1,2-Dichlorobenzene 3 of 13 22 sites (0.6%) 0 of 13 0

Ethyl acetate N/A --- 3 of 13 14 sites (22%)

Ethyl benzene 8 of 13 727 sites (19%) 1 of 13 (Chemical) 6 sites (9%)

Ethyl ether N/A --- 1 of 13 (Chemical) 1 site (2%)

2-Ethoxyethanol 3 of 13 14 sites (0.4%) 0 of 13 0

Isobutyl alcohol N/A --- 2 of 13 (Chemical, 

Furniture) 2 sites (3%)

Methanol 9 of 13 1,328 sites 

(34%) 4 of 13 23 sites (35%)

Methyl ethyl ketone 9 of 13 * 1,088 sites 

(28%) 3 of 13 14 sites (22%)

Methyl isobutyl ketone 9 of 13 534 sites (14%) 1 of 13 (Chemical) 5 sites (8%)

Methylene chloride 8 of 13 270 sites (7%) 1 of 13 (Chemical) 10 sites (15%)

Nitrobenzene 1 of 13 (Chemical) 16 sites (0.4%) 0 of 13 0

2-Nitropropane 1 of 13 (Chemical) 2 sites (0.1%) 0 of 13 0

Pyridine 1 of 13 (Chemical) 35 sites (0.9%) 1 of 13 (Chemical) 1 site (2%)

Tetrachloroethylene 7 of 13 135 sites (3%) 2 of 13 (Chemical, 

Electronics) 2 sites (3%)

Toluene 10 of 13 1,635 sites 

(42%) 3 of 13 33 sites (51%)

1,1,1-Trichloroethane 4 of 13 13 sites (0.3%) 0 of 13 0

1,1,2-Trichloroethane 1 of 13 (Chemical) 15 sites (0.4%) 0 of 13 0

Trichloroethylene 7 of 13 249 sites (6%) 4 of 13 7 sites (11%)

1,1,2-Trichlorotrifluoroethane (Freon 

113)

3 of 13 12 sites (0.3%) 0 of 13 0

Trichlorofluoromethane 2 of 13 (Chemical, 

Plastics) 14 sites (0.4%) 1 of 13 (Chemical) 1 site (2%)

Xylenes (total) 10 of 13 1,577 sites 

(40%) 4 of 13 21 sites (32%)

N/A: Chemical is not reported in TRI.

* TRI data for methyl ethyl ketone is for 2003; effective for the 2004 reporting year, methyl ethyl ketone was removed 

from the EPCRA list.
87

Data Relevant to Industrial Laundries

Industrial laundries are not required to report to TRI. However, when a non-laundry facility 

submits a report for a chemical, it must identify all offsite locations where the chemical is managed. 

Several facilities identified that they send TRI chemicals to industrial laundries. TRI chemicals 

may be present in various textiles (not limited to shop towels) which are serviced by the laundry.

TRI data from Envirofacts was used to identify industrial laundries receiving TRI chemicals. Data 

was selected from the ‘Chemical Transfer to Outside Locations’ table of the Envirofacts EZ query 

tool. A search was conducted for 45 industrial laundry facilities (these 45 industrial laundries were 

identified from the 2003 Biennial Report), using the facility’s RCRA ID number. These 45 sites 

were not expected to represent the entire universe of industrial laundries, but instead were expected 

to include the largest facilities and the sites most likely to be identified in the TRI. Using a much 

longer list of industrial laundries would require a much greater level of effort for a possibly 

diminishing return. The search was limited to include only those reports submitted for 2004 (or 

2003, in the case of methyl ethyl ketone).

TRI data relevant to industrial laundries are summarized in Exhibit E-4. These data identify the 

quantities of chemicals sent to industrial laundries (as estimated by the generator, not by the 

laundry). Five industrial laundries were identified. These laundries most likely collect textiles from 

other sites that are not reflected in this table for one or more of the following reasons: (1) other 

generating facilities were not required to report to TRI, or (2) other generating facilities who report 

to TRI did not estimate the quantities of chemicals present in these textiles. It is also significant to 

note that the TRI data, as reflected in Table E-4, do not identify the fate of the chemicals at the 

industrial laundry (e.g., solvent recovery practices, if any, are not known).

As stated above, Exhibit E-4 likely understates the universe of laundries in the TRI because a search 

was not conducted for all laundries. The source of the chemicals in the textiles sent to laundries is 

not known (e.g., uniforms, mats, shop towels), but it is reasonable to assume that at least some of 

this quantity is the result of shop towel use.

Exhibit E-4. Solvent Chemicals Sent to Industrial Laundries, 2004 TRI

Chemical Name

Number of 

Generating Sites

Transfer 

Amount, lb

Receiving 

Laundry RCRA 

ID Number

Ethyl benzene 2

1 OHD981784960

110 OHD981784960

Total: 111 1 site

Methanol 3

43 CTD134204916

0 OHD981784960

38 OHD981784960

Total: 81 2 sites

Methyl ethyl ketone * 2

0 OHD981784960

16 OHD981784960

Total: 16 1 site

Toluene 6

32 CTD134204916

29205 OHD981784960

2558 OHD037507282

1 OHD981784960
88

Chemical Name

Number of 

Generating Sites

Transfer 

Amount, lb

Receiving 

Laundry RCRA 

ID Number

8405 PAD014631246

22 OHD981784960

Total: 40,223 4 sites

Trichloroethylene 1 1673 OHD037507282

Total: 1,673 1 site

Xylene (mixed isomers) 5

250 NYD013250618

45 OHD981784960

1 OHD981784960

50 PAD014631246

300 OHD981784960

Total: 646 4 sites

TOTAL: 6 chemicals

7 unique generating 

sites Total: 42,750 5 unique laundries

* Data for methyl ethyl ketone are for 2003. All other data are 2004.

Industrial laundries are subject to reporting under Massachusetts TURA (if they otherwise meet the 

reporting criteria) and several reported in 2004. However, only one laundry facility reported any 

one of the 30 F-listed chemicals. This facility reported tetrachloroethylene, which is most likely 

from its associated dry cleaning operation. Therefore, the data in Massachusetts TURA were not 

used in estimating F-listed solvents received or managed at industrial laundries.
89

Appendix F. Industrial Laundry Data
90

Exhibit F-1. Towel Production and Washwater Generation Quantities at Industrial 

Laundries

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 785,783 345,455 2.05

 847,220 287,182 2.1

 847,841 243,380 5.6

 779,715 202,197 1.92

 738,666 375,742 1.49

 726,302 401,300 1.44

 856,501 367,466 1.57

 642,804 324,236 2.19

 241,567 344,200 2.02

 620,992 385,840 1.72

 445,718 298,200 1.71

 308,951 285,880 1.82

 330,060 140,000 2.51

 440,226 225,000 2.45

 374,177 225,000 2.81

 354,075 148,000 3.46

 283,752 83,000 1.74

 188,343 182,391 1.81

 1,107,482 168,686 1.8

 1,165,925 260,530 2.64

 1,184,754 321,968 1.77

 1,190,317 255,370 1.76

 1,111,143 318,511 2.19

 204,675 262,617 2.17

 408,855 36,513 2.14

 388,785 6,628 2.64

 181,840 31,336 2.4

 570,713 11,982 2.25

 417,125 14,699 1.96

 447,210 18,770 2.58

 648,728 26,869 2.76

 654,407 2,578 1.19

 261,749 285,604 1.43

 284,483 364,304 1.4

 298,687 378,876 1.48

 313,875 423,562 1.48

 15,997 452,315 2.2

 414,318 279,569 2.3

 396,143 284,299 2.2

 377,231 18,016 2.2

 409,672 19,462 2.1

 458,811 21,661 1.89

 553,085 19,200 1.91
91

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 396,496 7,025 1.942

 381,499 75,045 2.013

 369,898 258,850 2.183

 458,738 94,684 1.457

 424,346 86,457 1.857

 428,436 128,316 1.906

 404,900 65,703 1.35

 206,653 86,250 2.011

 447,753 191,108 1.65

 835,520 256,239 1.7

 263,225 270,095 1.6

 405,495 214,379 1.52

 356,253 42,073 1.64

 366,815 271,217 1.72

 256,437 376,666 1.35

 396,900 315,821 1.93

 352,829 268,425 1.32

 296,249 297,450 1.23

 305,390 62,076 1.53

 459,650 56,970 2.711

 137,782 61,339 2.241

 1,106,177 50,833 2.224

 967,976 69,609 2.194

 684,425 6,840 2.509

 579,938 7,150 1.375

 592,650 8,800 2.03

 208,466 9,240 1.289

 254,105 9,550 1.999

 239,674 52,370 1.687

 235,111 59,495 1.606

 229,097 67,860 1.56

 658,980 68,650 1.506

 853,130 80,815 1.862

 956,824 72,625 1.443

 987,872 21,900 1.564

 837,080 33,975 1.313

 85,305 44,625 1.474

 87,485 140,360 1.37

 88,750 231,150 1.523

 90,455 319,490 1.894

 89,760 467,677 1.644

 275,898 525,741 1.825

 289,830 12,500 1.173

 358,440 89,630 1.872

 366,360 93,604 1.461
92

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 414,000 58,314 0.827

 898,135 68,264 3.949

 967,555 1,000 1.571

 1,015,900 387,900 2.44

 1,002,680 449,100 1.722

 844,215 524,700 1.925

 96,000 371,700 2.045

 156,000 146,013 1.694

 225,300 120,022 2.407

 208,200 125,495 0.468

 1,211,190 116,629 0.666

 1,205,682 176,266 1.497

 1,036,810 48,995 2.4

 877,834 273,159 1.766

 810,151 122,120 2.084

 331,617 44,650 2.499

 438,953 51,918 1.156

 507,690 54,320 1.7

 5,594 61,884 1.593

 403,053 5,949 1.858

 472,030 291,493 2.029

 24,199 473,315 0.857

 613,722 445,271 2.033

 501,543 308,842 2.047

 300,309 172,770 1.996

 492,602 481,080 1.566

 394,556 114 1.497

 390,308 180,353 1.497

 324,657 140,180 1.497

 871,392 2,318,550 1.497

 8,231 1.497

 599,451 2.77

 1,433,778 2.8

 34,031 2.41

 429,974 2.26

 515,234 1.72

 419,030 2.97

 934,666 3.14

 724,241 2.67

 637,122 2.48

 372,196 2.77

 554,248 2.12

 553,057 3

 593,564 3.19

 485,498 3.83
93

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 979,336 3.75

 104,128 2.2

 776,647 3.94

 221,191 3.24

 607,947 2.53

 379,240 3

 613,590 3

 662,879 2.67

 422,969 3.35

 208,478 3.38

 383,188 2.93

 398,421 3.29

 595,078 2.58

 546,068 2.76

 462,571 

 368,368 

 310,823 

 276,235 

 264,404 

 346,920 

 377,609 

 175,117 

 678,467 

 406,335 

 156,795 

 240,265 

 348,927 

 445,411 

 577,722 

 774,727 

 609,533 

 459,292 

 580,652 

 9,420 

 378,580 

 366,441 

 184,999 

 440,695 

 891,549 

 489,839 

 284,868 

 718,155 

 585,300 

 223,501 

 890,139 
94

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 521,590 

 612,348 

 2,137,665 

 264,010 

 310,460 

 600,627 

 209,447 

 318,640 

 215,172 

 239,355 

 245,294 

 120,294 

 190,747 

 692,472 

 221,105 

 460,898 

 411,542 

 532,633 

 336,353 

 638,985 

 436,430 

 248,777 

 317,907 

 779,101 

 598,655 

 860,398 

 654,967 

 207,740 

 187,989 

 256,499 

 192,338 

 173,993 

 306,139 

 473,943 

 405,841 

 65,455 

 481,069 

 585,560 

 358,663 

 204,417 

 255,380 

 197,292 

 562,342 

 244,485 

 249,692 
95

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 2,330,436 

 123,985 

 710,097 

 360,887 

 368,275 

 264,598 

 343,472 

 176,285 

 467,758 

 314,355 

 429,080 

 350,686 

 398,695 

 76,007 

 385,334 

 171,306 

 309,286 

 234,662 

 563,694 

 455,192 

 812,963 

 271,927 

 315,746 

 1,198,806 

 552,778 

 319,048 

 469,306 

 5,446 

 5,214 

 3,418 

 3,000 

 2,900 

 627,430 

 343,216 

 291,570 

 584,040 

 1,600 

 377,222 

 439,931 

 482,890 

 599,382 

 486,500 

 144,840 

 132,840 

 100,320 
96

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 90,000 

 345,600 

 153,024 

 176,556 

 132,048 

 182,016 

 823,368 

 214,056 

 197,376 

 230,844 

 630,924 

 32,400 

 57,648 

 57,816 

 75,600 

 344,268 

 205,440 

 246,972 

 230,280 

 280,656 

 1,139,604 

 166,608 

 198,972 

 226,932 

 188,664 

 754,644 

 63,792 

 71,376 

 71,016 

 308,640 

 124,800 

 137,220 

 128,640 

 145,500 

 650,400 

 81,228 

 90,972 

 89,952 

 445,044 

 116,160 

 122,184 

 117,600 

 122,400 

 498,000 

 113,040 
97

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 117,600 

 105,840 

 141,000 

 511,200 

 62,820 

 61,116 

 106,944 

 113,352 

 267,504 

 71,460 

 43,776 

 102,288 

 100,212 

 131,412 

 144,756 

 538,860 

 60,492 

 76,380 

 79,692 

 99,504 

 37,260 

 78,816 

 85,608 

 68,220 

 86,280 

 388,404 

 121,104 

 119,808 

 144,192 

 100,380 

 584,400 

 61,356 

 67,560 

 319,500 

 112,548 

 111,576 

 87,528 

 140,472 

 789,744 

 46,428 

 47,736 

 48,540 

 53,676 

 280,404 

 63,468 
98

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 62,664 

 61,752 

 59,184 

 195,132 

 48,960 

 63,444 

 61,272 

 79,380 

 138,372 

 61,548 

 68,664 

 77,064 

 107,988 

 402,396 

 86,268 

 92,124 

 116,544 

 502,092 

 121,992 

 142,572 

 169,872 

 480,420 

 122,880 

 127,728 

 78,732 

 52,140 

 246,996 

 101,952 

 79,848 

 60,468 

 44,796 

 59,940 

 32,316 

 61,284 

 58,572 

 82,176 

 518,628 

 101,412 

 116,400 

 160,980 

 390,000 

 79,536 

 86,460 

 89,820 

 61,356 
99

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 282,600 

 68,064 

 84,420 

 71,316 

 66,108 

 61,740 

 70,164 

 249,372 

 95,052 

 104,244 

 110,532 

 118,488 

 459,456 

 90,792 

 109,464 

 139,944 

 114,408 

 419,940 

 101,280 

 112,728 

 96,168 

 106,776 

 569,400 

 77,124 

 70,176 

 51,096 

 53,232 

 169,188 

 67,824 

 79,488 

 72,168 

 70,584 

 60,540 

 50,820 

 62,412 

 70,512 

 82,428 

 292,800 

 50,196 

 59,196 

 49,464 

 55,956 

 234,324 

 116,400 

 128,364 
100

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 129,336 

 135,780 

 377,076 

 38,640 

 40,836 

 39,816 

 60,708 

 198,900 

 18,948 

 27,684 

 31,056 

 31,992 

 115,644 

 102,180 

 90,900 

 85,980 

 102,948 

 391,380 

 42,504 

 37,824 

 52,260 

 41,856 

 177,252 

 92,304 

 91,860 

 77,892 

 77,460 

 454,440 

 133,248 

 152,196 

 166,272 

 148,092 

 511,920 

 87,804 

 102,132 

 130,956 

 134,124 

 470,880 

 159,732 

 154,836 

 174,840 

 763,320 

 154,272 

 175,332 

 176,940 
101

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 169,728 

 636,960 

 56,220 

 50,280 

 44,844 

 63,576 

 209,040 

 69,960 

 79,800 

 78,912 

 273,684 

 34,320 

 124,932 

 102,804 

 104,124 

 75,480 

 163,356 

 172,776 

 99,552 

 103,260 

 88,176 

 106,560 

 102,072 

 94,236 

 73,752 

 73,596 

 102,396 

 7,944 

 13,704 

 15,936 

 13,548 

 91,944 

 120,864 

 96,024 

 87,804 

 438,650 

 456,750 

 456,750 

 307,700 

 115,375 

 143,875 

 197,121 

 173,436 

 150,326 

 33,934 
102

Shop towel production, 

lb/yr

Printer towel 

production, lb/yr Washwater (gal/lb)

 328,212 

 137,986 

 156,257 

 141,169 

 146,322 

 146,847 

 30,655 

 352,169 

 353,070 

 323,796 

 396,193 

 108,892 

 402,300 

 477,900 

 124,200 

 334,800 

 199,800 

 342,900 

 517,320 

 313,200 

 664,200 

 410,400 

 480,840 

 348,820 

 275,400 

 529,200 

 378,000 

 261,900 

 1,191,920 

 1,104,000 

Source: TRSA, 2010.
103

Exhibit F-2. Industrial Laundry Shop Towel Data from Public Comments

Comment 

Number A

Name & Location Percent of 

Company’s 

Overall Revenue

Number of 

Shop Towels 

Delivered 

Annually

Number 

of Shop 

Towels at 

One Time

199 Roscoe Company – Chicago, IL 15 10,000,000 1,500,000

307 RentWear Inc. – North Canton, OH 4 - - 308 Huebsch Services – Eau Claire, WI 5 2,500,000 - 309 Budget Uniform Rental Supply –

Gardena, CA - - -

314 Alltex Uniform Rental Services, Inc. 

– Manchester NH

“significant’ 13,000,000

(“250,000/week

”)

-

315 Clean – St. Louis, MO 12 5,245,188 - 317 Miller’s Textile Services –

Wapakoneta, OH

4

(4% of volume) - -

326 Walker Towel and Uniform –

Kansas City, MO

35 20,000,000 900,000

327 Uniforms By Star – 2 Locations: 

Dallas, TX and Fort Worth, TX

20 26,000,000 45,000,00

0

333 Clean – Highland, IL 40 8,900,000 15,000,00

0

334 Clean – O’Fallon, MO 14 6,576,804 - 335 Clean – Webb City, MO 15 4,500,000 200,000

339 Eagle Uniform & Linen Supply –

Lander, WY

8 2,500,000 6,000,000

343 Ace Uniform – Baltimore, MD 10 4,000,000 400,000 to 

600,000

344 Overall Laundry Services, Inc.–

Unknown number of sites in 

Washington and Oregon

10 35,000,000 1,700,000

361 Band Box Cleaners & Laundry Inc. 

– Tomah, WI

“important” 2,000,000 125,000

- : No information in public comments. 

A. All documents are from Docket RCRA-2003-0004. Comments dated March to April 2004.
104

Exhibit F-3. TCLP Results for Laundry Sludge

TCLP Concentration, mg/L

Location Date

F-Listed 

chemical 

detected? Cresol

TetrachloroethyleneChlorobenzeneEthylbenzene

1,1,1-

trichloroethane Xylenes

1,2-

Dichlorobenzene

Allentown 12/17/1997 no

Allentown 11/15/1995 yes 0.23

Allentown 11/12/1993 no

Allentown 11/20/1992 no

Auburn 7/8/1998 no

Bedford 2/24/1999 yes 0.08

Birmingham 10/7/1999 yes 0.017 0.079 0.163 0.009 0.181 0.08

Birmingham 10/30/1997 yes 0.056

Griffin 7/24/1986 no

Lakeland 4/9/1997 yes 0.28

Morgantown 1/30/1997 yes 1.88

Morgantown 10/6/1993 yes 0.004

Normal 3/10/1996 yes 0.678

Oklahoma City 10/22/1998 no

Pittsfield 6/9/1999 yes 0.11

Rock Hill 9/19/1999 no

Tulsa 1/29/1999 no

Plant 1 Sep-05 no

Plant 2 Jan-06 yes 0.06

Plant 3 Nov-04 yes 0.1

Plant 3 Dec-05 no

Plant 4 Mar-05 no

Maximum 1.88 0.08 0.079 0.163 0.009 0.181 0.678

Average 0.623 0.055 0.079 0.163 0.009 0.181 0.379

# of Samples 4 6 1 1 1 1 2

Source: UTSA, 2006.

Blanks indicate that the constituent was either not detected or not analyzed (source data did not indicate distinction).

Exhibit F-4. Number of Laundries Using a Single Landfill

State

Number of 

Laundries in State A

Number of 

Landfills B

Landfilled as % 

of all Discards C

Laundries D

per Landfill

Alabama 14 96.94

Alaska 4 96.97

Arizona 14 40 100.00 1

Arkansas 18 98.38

California 102 153 97.52 2

Colorado 16 72 100.00 1

Connecticut 11 2 14.38 2

Delaware 4 3 100.00 2

Florida 42 54 66.58 2

Georgia 28 57 98.80 2

Hawaii 8 63.97

Idaho 8 26 100.00 1

Illinois 42 52 100.00 2

Indiana 33 36 92.31 2
105

State

Number of 

Laundries in State A

Number of 

Landfills B

Landfilled as % 

of all Discards C

Laundries D

per Landfill

Iowa 14 59 97.85 1

Kansas 8 52 99.88 1

Kentucky 27 27 99.74 2

Louisiana 16 95.16

Maine 4 10 70.84 1

Maryland 20 31 71.43 2

Massachusetts 29 21 44.11 2

Michigan 36 51 91.66 2

Minnesota 17 23 63.56 2

Mississippi 6 17 100.00 1

Missouri 24 22 99.18 2

Montana 3 29 99.06 1

Nebraska 11 23 100.00 2

Nevada 9 23 100.00 1

New Hampshire 6 8 78.50 2

New Jersey 19 12 76.44 3

New Mexico 10 39 100.00 1

New York 51 26 65.79 3

North Carolina 35 41 98.89 2

North Dakota 1 14 100.00 1

Ohio 56 42 100.00 3

Oklahoma 15 31 91.68 2

Oregon 14 28 93.36 2

Pennsylvania 49 48 75.59 2

Rhode Island 4 2 99.77 3

South Carolina 13 18 93.39 2

South Dakota 4 15 100.00 1

Tennessee 16 35 100.00 2

Texas 72 189 99.87 1

Utah 6 34 94.87 1

Vermont 1 4 87.55 1

Virginia 21 60 82.33 1

Washington 13 19 94.13 2

West Virginia 7 18 100.00 1

Wisconsin 19 36 96.15 2

Wyoming 6 52 100.00 1

TOTAL 1006 1,654 89.65 2

Average 1.7

A. EPA 821-R-00-006 Technical Development Document for the Final Action Regarding

Pretreatment Standards for the Industrial Laundries Point Source Category (Revised March 2000). Table 4-1. Sites for 

District of Columbia are combined with Maryland.

B. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26-43. Table 9. Data not available for 

some states.

C. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26-43. Table 3.

This column represents the percent landfilled for discarded MSW only, versus that destined for combustion (i.e., 

excludes recycling). 
106

D. Minimum 1 laundry per landfill. Accounts only for laundries processing shop/ printer towels; accounts that waste is 

landfilled or combusted according to overall MSW percentage.

Exhibit F-5. Statistics Generated from Exhibit F-1: Towel Production and Washwater 

Generation Quantities at Industrial Laundries

Shop towels, lb/yr

Printer towels, 

lb/yr Washwater, gal/lb

Mean 312,893 196,605 2.06

Median 226,116 140,270 1.93

Range

Minimum 1,600 114 0.47

Maximum 2,330,436 2,318,550 5.6

Count 568 118 147

Note: TRSA (2010) included statistics for the data included in Exhibit F-1. The statistics for the shop towels and the 

washwater matched the values here. The statistics for the printer towels differed from those calculated by TRSA.
107

Appendix G. Review of Other Industry Data for Estimated Number of Generators
108

In 2006, EPA conducted additional research by reviewing (a) the references EPA collected in 

support of the 2003 economic analysis, (b) information on institutional users of wipes from EPA's 

Comprehensive Procurement Guidelines website (http://www.epa.gov/cpg/products/tissue.htm) 

(EPA, 2006j), and (c) data contained in EPA's 1994 “Evaluation of Volatile Organic Emissions 

Data for Non-process Solvent Use in 15 Commercial and Industrial Business Categories” (EPA600-SR-94-019) (EPA, 1994b). This research identified 10 additional industries totaling 380,062 

establishments, a fraction of which may use RCRA-regulated solvent wipes (2002 Economic 

Census establishment counts in parentheses):

 (1) NAICS 238220 HVAC service (87,936)

 (2) NAICS 23839 other building finishers (building renovation) (3,776)

 (3) NAICS 48819 aircraft maintenance & repair services (3,342)

 (4) NAICS 54138 laboratories (5,955)

 (5) NAICS 5417 scientific research (15,334)

 (6) NAICS 561720 janitorial services (50,984)

 (7) NAICS 61 schools & universities (49,319)

 (8) NAICS 721 hotels (60,949)

 (9) NAICS 8112 electrical equipment maintenance (14,567)

 (10) NAICS 92 public administration government agencies (87,900) 

Applying the same filtering (screening) percentages as summarized in Section 2.3.4 for the initial 

13 industrial sub-sectors, provides an estimate of 6,920 additional RCRA-regulated spent solvent 

disposable wipes generators (i.e., 380,062 x 33.5% x 67.1% x 8.1% = 6,920); added to the 8,106 

count for the 13 sub-sectors yields 15,026 spent solvent disposable wipes generators.

There is uncertainty with this estimate because the percentage of RCRA solvent use, wipe use, etc. 

in these additional industries was assumed to be the same as those for the 13 sectors initially 

evaluated. EPA does not have wipes-related information for these other 10 candidate industries. 

Nevertheless, the resulting estimate using this method (15,026 sites) is very close to the estimate 

using the Biennial Report data (14,475 sites).
109

Appendix H. Input Parameter Distributions for Disposable Wipes Analysis
110

Exhibit H-1. Distribution Assumptions for Input Parameters

Input parameter Number 

of Data 

Points

Distribution 

Selected, and Basis

Distributions 

Providing Good Fit

Relevant Exhibits

Number of wipes used 

per site per year in 

Other Industries

23 Lognormal (Best Fit)

Lognormal

Weibull

Gamma

H-3 and H-4

Number of wipes used 

per site per year in 

Printing

21 Gamma (Best Fit)

Gamma

Beta

Lognormal

Weibull

H-5 and H-6

Quantity of solvent 

used per wipe in Other 

Industries

7 Weibull (Best Fit) All H-7 and H-8

Quantity of solvent 

used per wipe in 

Printing

9 Weibull (Best Fit) All H-9 and H-10

Number of Generators 

per landfill 45 Gamma (Best Fit)

Lognormal

Gamma

Extreme value

Weibull

Beta

Logistic

H-11 and H-12

Fraction of Chemical 

(others) in solvent See Section 2.3.5 of report --- H-13 to H-28

‘Best Fit’ indicates highest rank fit according to the Anderson-Darling ranking method.

‘Good Fit’ indicates distributions that result in an Anderson-Darling value less than 1.5 (this value is provided in the 

Crystal Ball® documentation).

‘All’ indicates all 11 evaluated distributions (e.g., lognormal, normal, gamma) indicated a good fit.

‘None’ indicates that none of the distributions, including the ‘best fit’, provided a good fit.

Exhibit H-2. Summary of Outlier Tests

Input parameter Number of Data Points/ 

Outlier Test Method

Are Outliers Potentially 

Present?

Number of wipes used per 

site per year in Other 

Industries

23 - Dixon Yes - Highest value at 5% 

significance level

Number of wipes used per 

site per year in Printing 21 - Dixon No – No outliers

Number of Generators per 

landfill 45 - Rosner Yes – Highest value at 5% significance level

Outliers are evaluated at the 1%, 5%, or 10% levels using the Dixon or Rosner methods.

See Exhibits H-29 to H-34 for additional detail regarding the outlier tests. 
111

Exhibit H-3. Number of wipes used in Other Industries: Lognormal distribution

Exhibit H-4. Number of wipes used in Other Industries: Gamma distribution (alternate 

assumption)
112

Exhibit H-5. Number of wipes used in Printing: Gamma distribution

Exhibit H-6. Number of wipes used in Printing: Lognormal distribution (alternate 

assumption)
113

Exhibit H-7. Loading per Wipe, Other Industries: Weibull distribution 

Exhibit H-8. Loading per Wipe, Other Industries: Normal distribution (alternate 

assumption)
114

Exhibit H-9. Loading per Wipe, Printing: Weibull distribution

Exhibit H-10. Loading per Wipe, Printing: Normal distribution (alternate assumption)
115

Exhibit H-11. Number of Generators per Landfill: Gamma distribution

Exhibit H-12. Number of Generators per Landfill: Lognormal distribution (alternate 

distribution)
116

Exhibit H-13. Concentration of Acetone in Solvent: Exponential distribution

Exhibit H-14. Concentration of Ethyl Acetate in Solvent: Lognormal distribution
117

Exhibit H-15. Concentration of Ethyl Benzene in Solvent: Exponential distribution

Exhibit H-16. Concentration of 2-Ethoxy Ethanol in Solvent: Lognormal distribution
118

Exhibit H-17. Concentration of Methanol in Solvent: Lognormal distribution

Exhibit H-18. Concentration of Methyl Ethyl Ketone in Solvent: Beta distribution
119

Exhibit H-19. Concentration of Methylene Chloride in Solvent: Weibull distribution

Exhibit H-20. Concentration of MIBK in Solvent: Weibull distribution
120

Exhibit H-21. Concentration of n-Butanol in Solvent: Lognormal distribution

Exhibit H-22. Concentration of Tetrachloroethylene in Solvent Weibull distribution
121

Exhibit H-23. Concentration of Toluene in Solvent: Extreme Value distribution

Exhibit H-24. Concentration of Trichloroethylene in Solvent Weibull distribution
122

Exhibit H-25. Concentration of Xylene in Solvent: Exponential distribution

Exhibit H-26. Concentration of Methylene Chloride in Solvent: Normal distribution 

(alternate assumption)
123

Exhibit H-27. Concentration of Acetone in Solvent: Normal distribution (alternate 

assumption)

Exhibit H-28. Concentration of 2-Ethoxy ethanol in Solvent: 

Normal distribution (alternate assumption) 
124

Exhibit H-29. Rosner's Outlier Test for Number of Generators Per Landfill

Number of data: 45

Number of suspected outliers: 1

Mean sd

Potential 

outlier

Test 

value

Critical 

value (5%)

Critical 

value (1%)

18.98 13.19 67.00 3.64 3.09 3.44

For 5% Significance Level, there is 1 Potential Outlier

Therefore, Observation 67 is a Potential Statistical Outlier

For 1% Significance Level, there is 1 Potential Outlier

Therefore, Observation 67 is a Potential Statistical Outlier

Exhibit H-30. Number of Generators per Landfill
125

Exhibit H-31. Dixon's Outlier Test for Number of Wipes in Other Industries

Number of data = 23

10% critical value: 0.374

5% critical value: 0.421

1% critical value: 0.505

1. Data Value 400000 is a Potential Outlier (Upper Tail)?

Test Statistic: 0.446

For 10% significance level, 400000 is an outlier. 

For 5% significance level, 400000 is an outlier.

For 1% significance level, 400000 is not an outlier.

2. Data Value 480 is a Potential Outlier (Lower Tail)?

Test Statistic: 0.032

For 10% significance level, 480 is not an outlier.

For 5% significance level, 480 is not an outlier.

For 1% significance level, 480 is not an outlier.

Exhibit H-32. Number of Wipes in Other Industries
126

Exhibit H-33. Dixon's Outlier Test for Number of Wipes in Printing

Number of data = 21

10% critical value: 0.391

5% critical value: 0.44

1% critical value: 0.524

1. Data Value 600000 is a Potential Outlier (Upper Tail)?

Test Statistic: 0.335

For 10% significance level, 600000 is not an outlier.

For 5% significance level, 600000 is not an outlier.

For 1% significance level, 600000 is not an outlier.

2. Data Value 1000 is a Potential Outlier (Lower Tail)?

Test Statistic: 0.005

For 10% significance level, 1000 is not an outlier.

For 5% significance level, 1000 is not an outlier.

For 1% significance level, 1000 is not an outlier.

Exhibit H-34. Number of Wipes in Printing
127

Appendix I. Input Parameter Distributions for Laundry Sludge Analysis
128

Exhibit I-1 summarizes the distributional assumptions used for some of the input parameters in the 

laundry sludge analysis. Exhibit I-1 shows the distribution selected, possible alternate distributions, 

and references to graphs showing how the source data compare to the assumed distributions. 

Exhibit I-2 summarizes the basis for modifying the input parameter data for two parameters as part 

of an uncertainty analysis described in Section 3.4.3.

Exhibit I-1. Distribution Assumptions for Input Parameters

Input parameter

Number 

of Data 

Points

Distribution 

Selected, and 

Basis

Distributions 

Providing 

Good Fit

Relevant 

Exhibits

Quantity of shop towels 

washed per laundry, 

lb/year

568 Gamma (Best 

Fit) None I-3, I-4

Quantity of printer towels 

washed per laundry, 

lb/year

118 Gamma (Best 

Fit) Gamma I-5, I-6

Washwater concentration 

for shop towels, mg/L 

(acetone)

3 Lognormal 

(Assumption) All

Exhibits 

I-7 to I28 for all 

11 

chemicals 

with data

Washwater concentration 

for shop towels, mg/L 

(toluene)

3 Lognormal 

(Assumption) All

Washwater concentration 

for printer towels, mg/L 

(acetone)

3 Lognormal 

(Assumption) All

Washwater concentration 

for printer towels, mg/L 

(toluene)

3 Lognormal 

(Assumption) All

Quantity of washwater 

generated, gal/lb 147 Lognormal (good fit)

Extreme value, 

lognormal, 

gamma, beta

I-33

Number of Laundries 

Disposing Sludge in a 

Single Landfill

46 Logistic (best 

fit) None I-34

‘Best Fit’ indicates highest rank fit according to the Anderson-Darling ranking method.

For concentrations, see Section 3.3.3 for the basis for selecting a lognormal distribution.

‘Good Fit’ indicates distributions which result in an Anderson-Darling value less than 1.5 (this value is provided in the 

Crystal Ball® documentation).

‘All’ indicates all 11 evaluated distributions (e.g., lognormal, normal, gamma) indicated a good fit.

‘None’ indicates that none of the distributions, including the ‘best fit’, provided a good fit.
129

Exhibit I-2. Summary of Outlier Tests

Input parameter Number of Data Points/ 

Outlier Test Method

Are Outliers Potentially 

Present?

Quantity of shop towels 

washed per laundry, lb/year 568 - Rosner

Yes - Highest value at 5% 

significance level (see 

Exhibits I-29, I-30)

Quantity of printer towels 

washed per laundry, lb/year 118 - Rosner

Yes - Highest value at 5% 

significance level (see 

Exhibit I-31, I-32)
130

Exhibit I-3. Quantity of Shop Towels Laundered: Gamma distribution

Exhibit I-4. Quantity of Shop Towels Laundered: Lognormal distribution (alternate 

assumption)
131

Exhibit I-5. Quantity of Printer Towels Laundered: Gamma distribution

Exhibit I-6. Quantity of Printer Towels Laundered: Lognormal distribution (alternate 

assumption)
132

Exhibit I-7. Chlorobenzene Concentration in Shop Towel Washwater

Exhibit I-8. Chlorobenzene Concentration in Printer Towel Washwater
133

Exhibit I-9. Ethylbenzene Concentration in Shop Towel Washwater

Exhibit I-10. Ethylbenzene Concentration in Printer Towel Washwater
134

Exhibit I-11. Methylene Chloride Concentration in Shop Towel Washwater (distribution not 

used)

Exhibit I-12. Methylene Chloride Concentration in Printer Towel Washwater
135

Exhibit I-13. Tetrachloroethylene Concentration in Shop Towel Washwater (distribution not 

used)

Exhibit I-14. Tetrachloroethylene Concentration in Printer Towel Washwater
136

Exhibit I-15. Toluene Concentration in Shop Towel Washwater

Exhibit I-16. Toluene Concentration in Printer Towel Washwater
137

Exhibit I-17. Trichloroethylene Concentration in Shop Towel Washwater

Exhibit I-18. Trichloroethylene Concentration in Printer Towel Washwater
138

Exhibit I-19. MEK Concentration in Shop Towel Washwater

Exhibit I-20. MEK Concentration in Printer Towel Washwater
139

Exhibit I-21. Acetone Concentration in Shop Towel Washwater

Exhibit I-22. Acetone Concentration in Printer Towel Washwater
140

Exhibit I-23. MIBK Concentration in Shop Towel Washwater

Exhibit I-24. MIBK Concentration in Printer Towel Washwater
141

Exhibit I-25. Xylene Concentration in Shop Towel Washwater

Exhibit I-26. Xylene Concentration in Printer Towel Washwater
142

Exhibit I-27. Cresol Concentration in Shop Towel Washwater

Exhibit I-28. Cresol Concentration in Printer Towel Washwater
143

Exhibit I-29. Rosner's Outlier Test for Quantity of Shop Towels Washed per Laundry

Number of data: 568

Number of suspected outliers: 1

Mean sd

Potential 

outlier

Test 

value

Critical 

value (5%)

Critical 

value (1%)

312,893 289,957 2,330,436 6.958 -3.869 4.249

For 5% Significance Level, there is 1 Potential Outlier

Therefore, Observation 2,330,436 is a Potential Statistical Outlier

For 1% Significance Level, there is 1 Potential Outlier

Therefore, Observation 2,330,436 is a Potential Statistical Outlier

Exhibit I-30. Quantity of Shop Towels Washed per Laundry
144

Exhibit I-31. Rosner's Outlier Test for Quantity of Printer Towels Washed per Laundry

Number of data: 118

Number of suspected outliers: 1

Mean sd

Potential 

outlier

Test 

value

Critical 

value (5%)

Critical 

value (1%)

196,605 244,668 2,318,550 8.673 3.43 3.8

For 5% Significance Level, there is 1 Potential Outlier

Therefore, Observation 2,318,550 is a Potential Statistical Outlier

For 1% Significance Level, there is 1 Potential Outlier

Therefore, Observation 2,318,550 is a Potential Statistical Outlier

Exhibit I-32. Quantity of Printer Towels Washed per Laundry
145

Exhibit I-33. Quantity of Washwater Generated (gallon/pound towel): Lognormal 

distribution

Exhibit I-34. Number of Laundries Disposing Sludge in a Single Landfill: Logistic 

distribution
146

Appendix J. Bootstrap Estimates of 95% Confidence Intervals of Estimated Landfill 

Loadings
147

As described in Section 2.4 of this report, the estimated landfill loadings for the disposable wipe 

scenario are numerical values that are uncertain. There are different types of uncertainty associated 

with these landfill loading calculations, including uncertainty in the values used as input parameters

(Section 2.4.4), uncertainty in the model (Section 2.2), and uncertainty in the scenario. 

The uncertainty that exists arises from several places, including the analyzed scenario’s potential 

deviation from real-world conditions, lack of perfect knowledge of the values that should be used in 

the landfill loadings calculations, and the lack of ability to predict future conditions. 

If we were able to quantify these uncertainties, a decision maker could gain some sense of the 

magnitude of the influence of these uncertainties on the numbers in Exhibit 2-11 (for disposable 

wipes) and Exhibit 3-8 (for laundry sludge). In the absence of uncertainty, these numbers would 

either increase or decrease. Examples of ways that we could decrease the uncertainty would be:

• if more/better information were available to decrease parameter uncertainty, or

• alternative analyses of other plausible scenarios were performed in order to decrease scenario 

uncertainty, or

• the effects of the model’s approximation of reality could be estimated numerically, to reduce 

model uncertainty.

Many aspects of uncertainty are not easily quantifiable. There are also many ways to look at 

uncertainty. An additional method we considered was a “bootstrapping” technique, which is 

statistical technique that develops more information with which to interpret the possible magnitude 

of uncertainty that is associated with the parameter uncertainty in the landfill loadings calculations. 

(Efron and Tibshirani (1993)). This technique allows a risk assessor to use a distribution of values, 

such as the model outputs from the landfill loadings calculations, to develop a statistical descriptor 

of that sample, via a “resampling” technique. An example is the use of this technique to develop 

estimates of the confidence interval surrounding a 90th percentile landfill loading estimate. The 

bootstrap simulation is an attempt to quantify the range of estimates that are plausible alternative 

90th percentile estimates, given that the values used for each variable in the analysis are a limited 

representation of the actual situation.

Bootstrapping is a computational technique that involves “re-sampling” a distribution of values in 

order to estimate certain statistics associated with that distribution (such as the distribution’s 

estimated mean, standard error, or confidence intervals around percentile estimates). Typically, 

when described in textbooks that explain the basis for the bootstrapping technique, the distribution 

in question is a representative sample of empirical data, or observations of some type (e.g., test 

scores, hormone levels, or measurements of strength using some defined methodology). The 

distribution is “resampled,” with replacement, repeatedly in order to generate various estimators 

such as estimated standard errors, or estimated confidence intervals (Efron and Tibshirani (1993)). 

Extending the approach described above to a data set such as the outputs from the landfill loadings 

calculation model described in Section 2.4 could potentially be a viable method for developing 

estimated confidence intervals around the 90th percentile landfill loadings estimates. Although 

bootstrap techniques generally should be applied at the input level of a model, we applied this 

approach to the output distributions using an implicit assumption that uncertainty in inputs was the 

major source of variation in the model output. Specifically, we used a bootstrap simulation to 

develop 95 percent confidence intervals (CI) around the 50th and 90th percentile estimated landfill

loadings of solvents on disposed wipes.
148

Typically, bootstrap simulations are repeated a number of times to evaluate the numerical stability 

of the output distribution, by comparing results among the multiple bootstrap simulations. For the 

solvent specific landfill loadings distribution for disposed wipes (which is larger than the 

contribution from laundry sludge, based on a comparison of Exhibits 2-11 and 3-8), we performed 

the following steps for bootstrapping:

1. A random sample, referred to as the “bootstrap sample,” was generated from the cumulative 

probability distribution functions at sample sizes of 5, 10, 50, and 100.

2. The maximum likelihood estimation approach was used to fit a lognormal probability 

distribution to the bootstrap samples, assuming that a lognormal distribution was an appropriate 

choice for solvent concentrations on disposed wipes.

3. We further sampled 10,000 values from each lognormal distribution and estimated the 50th and 

90th percentiles.

4. We repeated steps 2 and 3 above 1,000 times to generate a sampling distribution around the 

selected percentiles. 

5. We estimated the 95 percent confidence intervals (i.e., values between 2.5th and 97.5th

percentiles) for the selected percentiles. These ranges represent uncertainty associated with 

estimates of those percentiles.

If we could quantify the uncertainty, a decision maker would be able to discern by how much the 

results would increase or decrease. The methodology developed to generate the estimated landfill 

loadings distributions included a sensitivity analysis (Section 2.4.2) and a semi-quantitative 

uncertainty characterization (various parts of Section 2.4). However, the uncertainty that results 

from lack of perfect knowledge of the model input variable values is not represented quantitatively 

in the model outputs since the uncertainty was not carried through to the model output stage. As a 

result, the estimated landfill loadings at selected percentiles represent a best estimate of the actual 

value at each percentile. An example of the source of this uncertainty – lack of perfect knowledge 

of the values that should be used for the model inputs – can be shown with the model input “solvent 

loading per wipe” in the “printing” sector. The variable “solvent loading per wipe” in the printing 

sector was represented as ranging from 9 to 86 grams, based on nine values from available data 

sources (Section 2.3.2). If the nine values of solvent loading per wipe used in this analysis were not 

representative of all printing solvent loadings per wipe, then the impact of using these nonrepresentative data on the 90th percentile estimated landfill loading is left unstated when only that 

90th percentile estimated landfill loading is presented. Thus, the 90th percentile estimated landfill 

loadings shown in Exhibits 2-11 and 3-8 are simply “best” estimates, and do not reflect (in a 

quantified way) any uncertainty that they contain.

Exhibits J-1 through J-20 present the following information. For illustration, the results for acetone 

(Exhibit J-1) are discussed although the same type of information is presented in each graph:

• The curve presents the full range of percentile values reflected in the results summarized in 

Exhibit 2-8 (50th percentile value of approximately 1,000 kg and 90th percentile value of 

approximately 9,000 kg for acetone).

• The dot-with-error-bar in the graph presents the estimated 95% confidence intervals for the 90th

and 50th percentile estimated landfill loadings shown in Exhibit 2-8.

• The estimated 95% confidence intervals stated below each graph show the values of the error 

bar reflected in the graph, rounded to one significant figure.
149

o The higher of the two dot-with-error-bar combination represents the 90th percentile 

estimated landfill loading together with its bootstrap-estimated 95% confidence interval (i.e., 

9,000 to 40,000 kg for acetone).

o The lower dot-with-error-bar combination represents the 50th percentile estimated landfill 

loading together with its bootstrap- estimated 95% confidence interval (i.e., 700 to 2,000 kg 

for acetone).

Exhibit J-1. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for acetone

Acetone

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 9,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 700 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 2,000 kg/yr
150

Exhibit J-2. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for benzene

Benzene

0.00

0.20

0.40

0.60

0.80

1.00

0.0 1.0 1,000.0 1,000,000.0

Estimated Mass Loading Rate (kg/Yr)

Cumulative 

Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 10 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 50 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 2 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 3 kg/yr

J-3. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for n-butanol

Butanol

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 500 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 3,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 50 kg/yr 

Estimated upper bound 95% confidence interval on 50th percentile estimate: 100 kg/yr
151

J-4. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for chlorobenzene

Chlorobenzene

0.00

0.20

0.40

0.60

0.80

1.00

0.001 1 1000 1000000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 400 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 40 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 90 kg/yr

J-5. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for cresols

Cresols

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 50 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 300 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 10 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 20 kg/yr
152

J-6. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for cyclohexanone

Cyclohexanone

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 1,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 100 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 300 kg/yr

J-7. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for 1,2-dichlorobenzene

1, 2-Dichlorobenzene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 300 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 30 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 80 kg/yr
153

J-8. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for 2-ethoxyethanol

2-Ethoxyethanol

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 200 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 400 kg/yr

J-9. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for ethyl acetate

Ethyl Acetate

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 400 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 40 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 100 kg/yr
154

J-10. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for ethylbenzene

Ethyl Benzene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 100 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 300 kg/yr

J-11. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for isobutyl alcohol

Isobutyl alcohol

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 200 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 800 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 20 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 50 kg/yr
155

J-12. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for methanol

Methanol

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 200 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 400 kg/yr

J-13. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for methyl ethyl ketone

Methyl Ethyl Ketone

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 70,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 1,000 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 2,000 kg/yr
156

J-14. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for methylene chloride

Methylene Chloride

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 3,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 400 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 800 kg/yr

J-15. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for methyl isobutyl ketone

Methyl Isobutyl Ketone

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 8,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 100 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 300 kg/yr
157

J-16. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for tetrachloroethylene

Tetrachloroethylene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 700 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 5,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 100 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 200 kg/yr

J-17. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for toluene

Toluene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 8,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 1,000 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 2,000 kg/yr
158

J-18. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for 1,1,2-trichloroethane

1,1,2-Trichloroethane

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 300 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 1,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 30 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 70 kg/yr

J-19. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for trichloroethylene

Trichloroethylene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 7,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 200 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 500 kg/yr
159

J-20. Estimated 95% confidence intervals for 90th and 50th percentile estimated landfill 

loadings for xylenes

Xylene

0.00

0.20

0.40

0.60

0.80

1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)

Cumulative Probability

Estimated lower bound 95% confidence interval on 90th percentile estimate: 8,000 kg/yr

Estimated upper bound 95% confidence interval on 90th percentile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50th percentile estimate: 700 kg/yr

Estimated upper bound 95% confidence interval on 50th percentile estimate: 2,000 kg/yr